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Frontiers in Environmental Economics ·

Volume 5 - 2026 | https://doi.org/10.3389/frevc.2026.1825864

Green transition and productive structure in Latin America: the role of innovation, finance, and the energy matrix in ALADI countries

  • Ximena Morales-Urrutia1
  • Aracelly Núñez-Naranjo2
  • Melissa Solórzano1
  • Jefferson Napoleon Naranjo-Gaibor3
  • Patricia Acosta-Vargas4*corresponding author
  1. 1Facultad de Contabilidad y Auditoría, Universidad Técnica de Ambato, Ambato, Ecuador
  2. 2Centro de Investigación en Empresa, Sociedad y Tecnología-ESTec, Facultad de Ciencias de la Educación, Universidad Tecnológica Indoamérica, Ambato, Ecuador
  3. 3Universidad Estatal de Bolívar, Guaranda, Ecuador
  4. 4Intelligent and Interactive Systems Laboratory, Universidad de Las Américas, Quito, Ecuador
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Abstract

The green transition in Latin America takes place within structurally heterogeneous economies characterized by dependence on primary exports, uneven technological capabilities, and partially diversified energy matrices. This study examines how technological innovation, financial development, renewable energy consumption, trade openness, economic growth, and industrialization influence CO 2 emissions in member countries of the Latin American Integration Association (ALADI). A balanced panel dataset covering 11 ALADI countries for the period 2000–2021 was analyzed using a two-step System Generalized Method of Moments (System GMM) estimator. This approach addresses endogeneity, unobserved heterogeneity, and the dynamic persistence of emissions. Additional robustness analyses were conducted using pooled Ordinary Least Squares (OLS) and Random Effects (RE) estimators. The findings indicate that structural factors are the main determinants of CO 2 emissions. Industrialization and trade openness exert positive and statistically significant effects, highlighting the role of productive structure and international integration in shaping environmental outcomes. Economic growth is also positively associated with emissions, suggesting that production expansion remains linked to environmental pressure. In contrast, technological innovation, renewable energy consumption, and financial development do not exhibit statistically significant effects in the dynamic specification. The results suggest that the green transition in ALADI countries is not automatic and cannot be driven solely by technological progress or financial expansion. Persistent structural characteristics, including carbon-intensive productive systems and patterns of global economic integration, continue to constrain environmental sustainability. Achieving a successful transition therefore requires deeper transformations in productive structures, industrial policies, and energy systems to decouple economic growth from environmental degradation.

Keywords

1 Introduction

The contemporary climate crisis is not only an environmental problem but also a structural manifestation of the development models that have shaped the global economy over the past few decades. Sustained economic growth, articulated around energy matrices heavily dependent on fossil fuels, has consolidated a regime of accumulation that is highly carbon-dependent (IPCC, 2023; Newell and Paterson, 2010; Bessette and Crawford, 2022).

According to the International Energy Agency (IEA, 2023), fossil fuels have accounted for nearly 80% of global primary energy demand over the past two decades, reflecting the persistence of a global productive structure deeply anchored in non-renewable resources. Recent research emphasizes that energy transitions are not merely technological processes, but rather socioeconomic transformations closely linked to institutional structures, financial systems, and power relations that shape development trajectories (Bessette and Crawford, 2022; Montakhabi et al., 2023).

From a political economy perspective, the energy transition cannot be reduced to a technological process of energy-source substitution; instead, it entails a profound transformation of the accumulation regime, patterns of productive specialization, and financial systems that sustain investment (Jessop, 2015; Leach et al., 2015). Carbon dioxide (CO2) emissions—the principal component of greenhouse gases—thus reflect not only an environmental imbalance, but also the way in which economies organize their production systems, trade structures, and international economic integration (Newell, 2019).

Recent studies show that emission dynamics are closely associated with the institutional configurations that shape technological innovation, energy investment, and the productive structures of national economies (Wiedenhofer et al., 2020; Liu et al., 2024).

The Paris Agreement established ambitious targets to limit global temperature rise to below 2 °C, and promoted national commitments to transform energy systems (Rogelj et al., 2016). However, current emissions trajectories reveal a significant gap between commitments and actual outcomes (Calvin et al., 2023). This gap highlights that the green transition is also a matter of institutional capacity, financial orientation, and productive structure (Dauvergne, 2018). Recent literature suggests that the success of decarbonization strategies largely depends on financial systems' ability to mobilize investment toward clean technologies and renewable energy sources (Schreiner and Madlener, 2022).

In Latin America, the energy transition unfolds within a context characterized by structural heterogeneity, dependence on primary exports, and external vulnerability (Núñez-Naranjo and Morales-Urrutia, 2025; Furtado, 1970; Bértola and Ocampo, 2014; Prebisch, 1950). The region combines partially renewable energy matrices with productive structures heavily reliant on natural resources, creating tensions between international competitiveness and environmental sustainability (Svampa, 2019; Wanderley, 2018). Recent studies indicate that Latin American economies face particular challenges in advancing toward low-carbon development models, largely due to their persistent specialization in extractive sectors and limited technological diversification (Morales-Urrutia et al., 2025; Lakatos et al., 2024; Schouten et al., 2022). This configuration raises a central question for the sociology of development: Is it possible to achieve a decoupling between economic growth and carbon emissions without altering the structural foundations of the prevailing development model?

Recent literature has identified technological innovation, financial development, green finance, and trade openness as potential drivers facilitating the energy transition (Guo et al., 2021; Ozili, 2022). Empirical studies suggest that technological innovation can reduce emissions when directed toward clean technologies, although it may also increase energy consumption when it stimulates productive expansion (Coşkun et al., 2025; Józwik et al., 2022). Likewise, the expansion of renewable energy consumption has proven to be one of the most consistent mechanisms for reducing carbon intensity in both developed and emerging economies (Schreiner and Madlener, 2022; Magazzino et al., 2022). However, the effects of these factors depend largely on the pattern of productive specialization and the institutional framework guiding investment and environmental regulation (Newell, 2019; Baxter, 2000).

Within this context, the present study examines how the structural configuration of development—expressed through economic growth, technological innovation, financial development, trade openness, and renewable energy consumption—shapes environmental sustainability in the member countries of the Latin American Integration Association (ALADI) during the period 2000–2021. The objective is to evaluate whether the region shows evidence of decoupling between economic growth and carbon emissions, or whether it continues to reproduce carbon-intensive development trajectories, thereby contributing to the sociological debate on the political economy of the energy transition in emerging economies.

1.1 Background

1.1.1 Political economy of carbon and the regime of accumulation

The political economy of carbon argues that emissions are not an accidental byproduct of economic growth, but rather the result of specific institutional, technological, and productive configurations that structure development trajectories (Newell and Paterson, 2010). In this sense, energy systems reflect power relations, sectoral interests, and regulatory frameworks that shape investment strategies and the organization of production (Dauvergne, 2018).

Recent research emphasizes that energy transitions are deeply mediated by institutions, political coalitions, and economic structures, which influence both the direction of technological change and the pace of decarbonization. In peripheral economies, specialization in extractive sectors that are intensive in natural resources tends to reproduce carbon-dependent accumulation patterns (Montakhabi et al., 2023; Prebisch, 1950; Svampa, 2019). This dependence constrains the capacity for structural transformation and conditions the prospects for energy transition.

1.1.2 Ecological modernization

The ecological modernization theory proposes that, under certain institutional conditions, economic development can incorporate environmental considerations through technological innovation, effective regulation, and the reorganization of productive systems (Baxter, 2000; Jänicke, 2008). This approach suggests that the transition toward sustainable economies becomes feasible when adequate regulatory incentives, investment in clean technologies, and financial systems capable of mobilizing capital into environmentally sustainable sectors are in place.

Recent research has reinforced this perspective by demonstrating that adopting renewable energy, improving energy efficiency, and implementing climate policies can simultaneously reduce emissions and promote economic growth (Schreiner and Madlener, 2022). However, empirical evidence indicates that ecological modernization depends on strong state capacities and diversified productive structures, which pose significant challenges for emerging economies (Han et al., 2021). In contexts where regulatory and technological capacities remain limited, implementing ecological modernization strategies faces additional structural constraints.

1.1.3 Technological innovation and the direction of technical change

From the perspective of endogenous growth theory (Youl, 2026), innovation drives productivity and can reduce energy intensity. However, the direction of technical change is crucial: innovation may enhance productive efficiency without necessarily reducing total emissions (Acemoglu et al., 2012). Consequently, technological innovation contributes to sustainability only when it is institutionally oriented toward clean technologies.

Recent empirical research confirms that technological innovation can contribute to emission reductions when directed toward eco-innovation, energy efficiency, and low-carbon technologies (Khan et al., 2023). However, when innovation primarily supports productive expansion or energy-intensive consumption, it may have the opposite effect, increasing total emissions (Almulhim et al., 2025).

1.1.4 Financial development and green finance

Financial development plays a significant role in the sectoral allocation of capital and in an economy's capacity to finance innovation and structural transformation (Levine, 1997). In the context of the energy transition, financial systems are critical in channeling resources toward clean technologies, sustainable energy infrastructure, and climate mitigation projects.

When financial systems incorporate environmental criteria, they can accelerate the energy transition through instruments such as green bonds and sustainable credit mechanisms (Ozili, 2022). However, in contexts where financing continues to prioritize extractive activities, the financial system may reinforce carbon-intensive development trajectories (Newell, 2019).

1.1.5 International trade and productive specialization

Trade openness is an important factor in emission dynamics, facilitating technological transfer, innovation diffusion, and the reorganization of global value chains (Grossman and Krueger, 1995). From this perspective, international trade can contribute to improving energy efficiency and the diffusion of cleaner technologies across countries.

However, from a dependency theory perspective, the international integration of peripheral economies tends to reinforce patterns of specialization in natural resource- and energy-intensive sectors (Bértola and Ocampo, 2014). In emerging economies, trade expansion may therefore simultaneously facilitate technological diffusion while also reinforcing carbon-intensive extractive activities.

1.1.6 Economic growth and decoupling

The concept of decoupling refers to the possibility of reducing environmental pressures without constraining economic growth through improvements in energy efficiency, technological innovation, and changes in productive structures (UNEP, 2011). Within this framework, decoupling can be relative, when emissions grow more slowly than the economy, or absolute, when emissions decline while economic output continues to increase.

At the global level, absolute decoupling remains limited and depends on profound structural transformations in the energy matrix, production systems, and consumption patterns (Wiedenhofer et al., 2020). Furthermore, research on energy transitions shows that sustainable decoupling requires coordinated public policies that integrate technological innovation, green finance, and the structural transformation of productive systems (Altinoz et al., 2020; Zhou et al., 2018).

2 Materials and methods

2.1 Research design and analytical strategy

To capture the temporal persistence of the environmental indicator and mitigate potential biases arising from endogeneity and unobserved heterogeneity, a dynamic model was estimated using the two-step System Generalized Method of Moments (System GMM). This methodological choice is grounded in the dynamic nature of the dependent variable and the potential simultaneity between the explanatory variables and the error term. In this context, the inclusion of the lagged dependent variable introduces correlation with unobserved effects, thereby undermining the validity of conventional panel-data estimators.

In this regard, the System GMM estimator addresses these limitations by using internal instruments, thereby ensuring consistent and efficient estimates within a panel data framework. Before estimation, the stationarity of the variables was verified using the Im–Pesaran–Shin (IPS) unit root test, supporting the statistical validity of the model. Additionally, the absence of second-order autocorrelation was confirmed via the Arellano–Bond test, while instrument validity was corroborated using the Hansen test. Taken together, these diagnostics ensure the robustness and consistency of the obtained results.

To mitigate the risk of instrument proliferation, the GMM-style instruments were collapsed, thereby reducing the instrument count and avoiding overfitting bias, particularly given the relatively small cross-sectional dimension of the sample.

2.2 Sample and data sources

The initial sample includes the member countries of ALADI: Argentina, Bolivia, Brazil, Chile, Colombia, Cuba, Ecuador, Mexico, Panama, Paraguay, Peru, Uruguay, and Venezuela. However, Cuba and Venezuela were excluded due to the absence of complete statistical series for the period under analysis. As a result, the final dataset comprises a balanced panel of 11 countries with 220 annual observations spanning 2000–2021.

The data were obtained from the World Development Indicators (WDI) database of the World Bank, a widely used source in comparative studies of development and sustainability due to its methodological consistency, international coverage, and extensive collection of development indicators.

The WDI database compiles internationally comparable statistics on the economic, social, environmental, and institutional dimensions of development, covering hundreds of indicators across more than 200 economies and time series extending back several decades.

2.3 Variables

2.3.1 Dependent variable

Environmental sustainability is proxied by total carbon dioxide (CO2) emissions, excluding land use and land-use change (LULUCF), measured in million metric tons of CO2 equivalent. Although CO2 emissions do not capture the full spectrum of environmental degradation, they represent the primary indicator of climate impact associated with energy and productive systems. They are widely used in comparative studies of energy transition and environmental sustainability.

2.3.2 Independent variables

The explanatory variables represent structural dimensions of the development model:

*Technological Innovation (TI):* research and development (R&D) expenditure. This variable serves as a proxy for technological capacity and structural digitalization.

*Renewable Energy Consumption (REC):* share of total final energy consumption derived from renewable energy sources.

*Financial Development (FD):* Domestic credit to the private sector (as a percentage of GDP).

*Economic Growth (EG):* Annual growth rate of real gross domestic product (GDP).

*Trade Openness (TO):* Total trade (exports plus imports) as a percentage of GDP.

*Industrialization (I):* Medium- and high-technology exports (% of manufactured exports).

Together, these variables operationalize key dimensions of the political economy of development, including productive structure, technological orientation, international integration, and financial depth.

2.3.3 Model specification

The econometric model estimated in this study is expressed in Equation 1.

ΔCO2it=αΔ2CO2it-1+β1Δ2TIit-1+β2Δ2RECit-1 + β3ΔFDit-1+β4ΔEGit-4 + β5Δ2TOit-1 +β6Δ2Iit-2+εit(1)

Where:

  • - ΔC<sub>O2it</sub>: Change in carbon dioxide emissions.
  • - Δ2CO2it-1: Second difference of CO2 emissions lagged one period (capturing dynamic adjustment).
  • - Δ2TIit-1: Second difference of technological innovation with a two-period lag.
  • - Δ2RECit-1: Second difference of renewable energy consumption lagged one period.
  • - ΔFD*it*−1: First difference of financial development lagged one period.
  • - ΔEG*it*−4: First difference of economic growth with a four-period lag.
  • - Δ2TOit-1 :Second difference of trade openness, lagged by one period.
  • - Δ2Iit-2: Second difference of industrialization with a two-period lag.
  • - ε*it*: error term.

The lag structure adopted in the model reflects both theoretical considerations and the dynamic nature of the relationships between economic activity, productive structure, and environmental outcomes. In particular, longer lags are assigned to economic growth (L4) to capture delayed effects associated with structural changes in production, infrastructure expansion, and energy demand, as the environmental consequences of economic growth tend to materialize progressively over time. This specification is consistent with previous dynamic panel studies that employ longer lag structures for macroeconomic growth variables when analyzing environmental degradation and emissions persistence (Altinoz et al., 2020; Acheampong, 2018; Shahbaz et al., 2018). Industrialization is introduced with an intermediate lag (L2), reflecting the time required for changes in productive composition and export structures to influence emission patterns.

In contrast, variables such as financial development and renewable energy consumption are modeled with shorter lags (L1) or in differences, as their effects on economic activity, investment allocation, and energy use are expected to materialize more immediately. This differentiation in lag structure allows the model to capture heterogeneous temporal dynamics across structural and macroeconomic variables.

Furthermore, the use of lagged and differenced variables is consistent with the System GMM framework, which requires an appropriate temporal structure to address endogeneity and dynamic persistence while maintaining model parsimony. This specification balances theoretical consistency with empirical tractability, given the relatively small sample size.

3 Results

3.1 Descriptive statistics

The descriptive analysis provides an initial approximation of the structural heterogeneity among ALADI countries over the period 2000–2021. As shown in Table 1, CO2 emissions have a mean of 2.41 and a standard deviation of 1.16, indicating moderate variability in emission levels across the countries analyzed. The observed range, from 0.69 to 4.95, reflects heterogeneous environmental performance and differentiated carbon trajectories within the region.

Table 1. Descriptive statistics (2000–2021).

VariableMeanStd. dev.MinMax
Carbon dioxide CO2 emission2.4165811.1651320.69798684.959708
Technological innovation0.37716810.27288930.041.28
Renewable energy consumption30.043816.949067.770.4
Financial development41.0278924.9839.68123.56
Economic growth3.232844.378373−17.8212316.46712
Trade openness58.2289227.1260621.85226164.5863
Industrialization25.9225221.520911.19284281.72981

Technological Innovation shows a relatively low mean of 0.38 and a standard deviation of 0.27, with values ranging from 0.04 to 1.28. This limited dispersion suggests that investment in innovation remains modest across ALADI countries, reinforcing the idea of constrained technological capacity and uneven development of innovation systems.

Renewable energy consumption records a mean of 30.04%, a standard deviation of 16.95, and a wide range of 7.7% to 70.4%. These figures highlight substantial differences in the composition of national energy matrices, reflecting varying degrees of transition toward cleaner energy sources.

Financial Development, used as a proxy, has a mean of 41.03% of GDP and a relatively high standard deviation of 24.98, with values ranging from 9.68% to 123.56%. This case indicates significant disparities in financial system depth and credit access across the region.

Trade openness averages 58.23% of GDP, with considerable dispersion (standard deviation of 27.13%) and a range of 21.85% to 164.59%, suggesting heterogeneous levels of integration into international markets among ALADI countries.

Economic growth shows a mean of 3.23% and a standard deviation of 4.38, evidencing high volatility in macroeconomic performance. The range spans from a contraction of−17.82% to a peak growth rate of 16.47%, capturing both economic crisis and expansion in the sample.

Finally, industrialization presents a mean of 25.92, a standard deviation of 21.52, and a range of 1.19 to 81.73. This wide dispersion reflects substantial differences in productive structure across countries, confirming the presence of structural heterogeneity as a defining feature of the region.

3.2 Unit root test for ALADI countries

The unit root test results presented in Table 2, based on the Im–Pesaran–Shin (IPS) methodology, provide important evidence regarding the stationarity properties of the variables included in the model. Overall, the findings reveal a mixed order of integration across the panel, which justifies the use of dynamic panel data techniques.

Table 2. Unit root test for ALADI countries.

Im-Pesaran-Shin unit-root test
VariableNivelFirst difference
p-valuep-value
Carbon dioxide CO2 emission0.99980.000
Renewable energy consumption0.99720.000
Technological innovation0.99210.000
Financial development0.0141
Economic growth0.0259
Trade openness0.32290.000
Industrialization0.14710.000

Unit root test for ALADI countries.

At levels, most of the variables fail to reject the null hypothesis of a unit root. Specifically, CO2 emissions (p = 0.9998), renewable energy consumption (p = 0.9972), technological innovation (p = 0.9921), trade openness (p = 0.3229), and industrialization (p = 0.1471) are non-stationary, indicating that these variables follow stochastic trends over time. This behavior is consistent with structural and macroeconomic indicators, which tend to evolve persistently across countries.

In contrast, domestic credit to the private sector (p = 0.0141) and GDP growth (p = 0.0259) are stationary at levels, suggesting that these variables exhibit mean-reverting behavior. This case reflects their more cyclical and short-term nature compared to structural variables such as emissions, industrialization, and energy composition.

After first differencing, all previously non-stationary variables become stationary, as indicated by p-values equal to 0.000. This case confirms that CO2 emissions, renewable energy consumption, technological innovation, trade openness, and industrialization are integrated of order one I(1), while domestic credit and GDP growth are I(0).

The presence of both I(0) and I(1) variables indicates a mixed integration structure, which supports the appropriateness of employing dynamic panel estimators such as System GMM. These methods are robust to such conditions and allow consistent estimation while addressing potential endogeneity and data persistence.

Overall, the IPS test results confirm that the dataset satisfies the econometric requirements for panel data analysis and highlight the structural persistence of key variables associated with environmental sustainability and productive structure in ALADI countries.

3.3 Dynamic panel-data estimation, two-step system GMM

The dynamic panel-data model estimated using the two-step System GMM approach provides robust evidence on the determinants of CO2 emissions in ALADI countries over the study period. An important result of the dynamic specification is the negative, statistically significant coefficient on the lagged dependent variable, which provides evidence of adjustment dynamics in CO2 emissions across ALADI countries. This finding suggests partial convergence, in which countries with higher initial emission levels tend to experience relatively slower growth—or even reductions—in emissions over time. In this sense, emission trajectories are not purely divergent but tend to stabilize.

This adjustment process may be associated with gradual changes in production structures, improvements in energy efficiency, or the implementation of environmental policies. However, the extent of these mechanisms may vary across countries. From a policy perspective, this result implies that emission dynamics are not entirely path-dependent and that there is potential for coordinated policy interventions to accelerate convergence toward lower emission levels. This case highlights the importance of regional strategies that support structural transformation and the adoption of cleaner technologies.

Regarding the explanatory variables, technological innovation, lagged, is not statistically significant (p = 0.344). This result suggests that, in the context of ALADI countries, innovation processes do not immediately reduce emissions. It may reflect that innovation in the region is not sufficiently oriented toward environmental efficiency or that its effects materialize over longer time horizons.

Similarly, renewable energy consumption and financial development do not show statistically significant effects on CO2 emissions (p > 0.10). These findings may indicate structural limitations in both the energy transition process and the financial systems' capacity to support environmentally sustainable investments effectively. Despite progress in these areas, their short-term impact on emissions remains limited.

In contrast, economic growth shows a positive, marginally significant coefficient (β = 0.0083, p < 0.10), suggesting that increases in economic activity are associated with higher emissions. This result is consistent with growth patterns that remain dependent on energy-intensive production structures.

Trade openness shows a positive, statistically significant effect (β = 0.0049, p < 0.05), suggesting that greater integration into international markets may be associated with higher emissions. This case may be explained by specialization in carbon-intensive sectors or by the expansion of industrial activity driven by trade.

Finally, industrialization emerges as a key determinant of CO2 emissions, with a positive and highly significant coefficient (β = 0.0214, p < 0.01). This finding highlights the central role of productive structure in shaping environmental outcomes, confirming that industrial expansion remains a major driver of emissions in ALADI countries.

In terms of model validity, the Arellano-Bond test indicates the presence of first-order autocorrelation [AR(1), p < 0.05], which is expected in first-differenced models. In contrast, no second-order autocorrelation is detected [AR(2), p > 0.10], supporting the consistency of the estimators. Additionally, the Hansen test (p = 0.285) suggests that the instruments used are valid and not overidentified.

Importantly, the number of instruments (Rogelj et al., 2016) was kept comparable to the number of cross-sectional units, and the use of collapsed instruments helps ensure that the Hansen test remains informative despite the small sample size.

3.4 Robustness analysis and econometric interpretation

To ensure the reliability of the empirical findings, this study employs multiple estimation techniques, including the System GMM estimator (Table 3), pooled OLS (Table 4), and random effects (RE) (Table 5). This approach allows for a comprehensive assessment of the stability and robustness of the estimated relationships across different econometric specifications.

Table 3. Dynamic panel-data estimation, two-step system GMM.

Group variable: Country1Number of obs = 187
Time variable: Year1Number of groups = 11
Number of instruments = 11Obs per group: min = 17
F(7, 10) = 46.50 Prob > F = 0.000Avg = 17.00
D.Carbon dioxide CO2emissionCoef.Corrected sth. err.tP > /t/(95% conf. interval)
Carbon dioxide CO2 emission LD.−0.37118530.1416203−2.620.026−0.686735 −0.0556355
Technological innovation LD.0.11522560.11588320.990.344−0.1429783 0.3734294
Renewable energy consumption LD.−0.00103270.0152699−0.070.947−0.0350561 0.0329907
Financial development L1.−0.00010730.0004122−0.260.800−0.0010256 0.0008111
Economic growth L4.0.00828320.00380922.170.045−0.0002043 0.0167707
Trade openness LD.0.00497610.00165833.000.0130.0012812 0.008671
Industrialization L2D.0.0214110.0079122.710.0080.0040428 0.0388543
_cons0.00990440.02758860.360.727−0.051567 0.0713757
Instruments for first differences equation GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/2).LD.CarbondioxideCO2emission collapsed Instruments for levels equation Standard LD.CarbondioxideCO2emission L2D.Researchanddevelopmentexpendi LD.Renewableenergyconsumption L.Domesticcredittoprivatesecto L4.GDPgrowthannual LD.TradeofGDP L2D.Industrialization _cons GMM-type (missing=0, separate instruments for each period unless collapsed) D.LD.CarbondioxideCO2emission collapsed
Arellano-Bond test for AR(1) in first differences: z = −2.09 Pr > z = 0.037 Arellano-Bond test for AR(2) in first differences: z = −1.35 Pr > z = 0.177
Hansen test of overid. restrictions: chi2(3) = 3.79 Prob > chi2 = 0.285 (Robust, but weakened by many instruments.)

Table 4. OLS pulled.

Number of obs = 231 F(6, 224) = 2.64 Prob > F = 0.0172 R-squared = 0.0733 Root MSE = 1.1375
Carbon dioxide CO2 emissionCoef.Corrected sth. err.tP>/t/(95% conf. interval)
Technological innovation LD.−0.40800.4876−0.840.404(−1.3689; 0.5529)
Renewable energy consumption LD.−0.01980.0089−2.220.028(−0.0374; −0.0022)
Financial development L1.0.01140.00343.300.001(0.0046; 0.0183)
Economic growth L4.0.00870.00362.420.016(0.0016; 0.0158)
Trade openness LD.0.00490.00271.810.072(−0.0004; 0.0102)
Industrialization L2D.0.01750.00722.430.016(0.0033; 0.0317)
_cons2.04430.193110.590.000(1.6637; 2.4249)

Table 5. Random-effects GLS regression.

Random-effects GLS regressionNumber of obs = 231
Group variable: Country1Number of groups = 11
R-sq:Obs per group:
within = 0.3858min = 21
between = 0.0243avg = 21.0
overall = 0.0463max = 21
corr(u_i, X) = 0 (assumed)Wald chi2 (6) = 62.10 Prob > chi2 = 0.0000
(Sth. Err. adjusted for 11 clusters in Country1)
Carbon dioxide CO2 emissionCoef.Corrected sth. err.tP > /t/(95% conf. interval)
Technological innovation LD.0.06520.05211.250.212(−0.0370; 0.1674)
Renewable energy consumption LD.−0.02370.0071−3.340.001(−0.0376; −0.0098)
Financial development L1.0.01480.00324.630.000(0.0085; 0.0211)
Economic growth L4.0.01950.00434.530.000(0.0111; 0.0279)
Trade openness LD.0.00610.00282.180.029(0.0006; 0.0116)
Industrialization L2D.0.01520.00702.170.030(0.0015; 0.0289)
_cons1.78210.39524.510.000(1.0075; 2.5567)

Overall, the results reveal a consistent pattern in the direction of the relationships across models. In particular, economic growth, trade openness, and industrialization exhibit positive, statistically significant effects on CO2 emissions in both static (OLS and RE) and dynamic (System GMM) specifications. This consistency suggests that these variables represent structurally robust determinants of environmental degradation, reflecting the expansion of economic activity and production intensity in the analyzed economies.

Financial development has a positive, statistically significant effect in both OLS and random-effects estimations. However, this effect becomes statistically insignificant under the System GMM specification. This difference is not interpreted as a weakness but rather as evidence of endogeneity in the static models. Specifically, financial development may be simultaneously determined with economic growth and industrial activity, leading to biased estimates in OLS and RE models. By addressing this issue through appropriate instrumentation and dynamic specification, the System GMM estimator provides more reliable results. Therefore, the loss of significance suggests that the apparent effect of financial development in static models may be driven by reverse causality or omitted-variable bias rather than a direct causal impact on emissions.

A similar pattern is observed for renewable energy consumption. While it shows a negative, statistically significant effect in OLS and random-effects models, this relationship becomes statistically insignificant in the System GMM estimation. Nevertheless, the negative sign remains consistent across specifications, which aligns with theoretical expectations regarding the role of renewable energy in reducing emissions. The reduction in statistical significance indicates that the magnitude of this effect is sensitive to model specification and endogeneity correction, suggesting that its impact may be indirect or mediated by other structural factors.

Technological innovation does not present statistically significant effects across the different estimation methods. This result may reflect the limited orientation of innovation activities toward environmentally efficient technologies in the analyzed economies. In this context, innovation may be associated with productivity improvements rather than environmental performance, which explains the lack of a clear relationship with emissions.

Finally, the validity of the System GMM estimator is supported by the standard diagnostic tests. The Arellano-Bond test confirms the presence of first-order autocorrelation and the absence of second-order autocorrelation. In contrast, the Hansen test of overidentifying restrictions indicates that the instruments used are valid. These results reinforce the appropriateness of the dynamic panel approach and strengthen the credibility of the empirical findings.

4 Discussion

The results from estimating the dynamic model using the two-step System GMM method provide deeper insights into the structural determinants of CO2 emissions in ALADI countries. Contrary to expectations derived from ecological modernization theory, technological innovation does not exhibit a statistically significant effect on emissions. This finding suggests that, across the analyzed economies, innovation processes have not translated into observable environmental improvements in either the short- or medium-term. This result is consistent with recent studies showing that, in developing economies, innovation is not necessarily oriented toward sustainability but rather toward productive efficiency or growth expansion (Acemoglu et al., 2012; Aghion et al., 2012; Popp, 2019).

From the perspective of directed technical change, this outcome aligns with the argument that innovation does not automatically reduce emissions unless it is explicitly targeted toward clean and efficient technologies (Acemoglu et al., 2012). In this regard, the evidence suggests that, in ALADI countries, innovation is more closely associated with processes of productive modernization than with structural transformation toward sustainability. This finding is consistent with recent evidence from emerging economies, where innovation may increase energy consumption—and, consequently, emissions—when not oriented toward eco-innovation or the energy transition (Khan et al., 2023; Omri, 2020). Moreover, recent studies emphasize that the quality of innovation, rather than its volume, is the key determinant in reducing environmental footprints (Popp, 2019).

Regarding renewable energy consumption, the results of the dynamic model indicate that this variable does not exert a statistically significant effect on emissions, which contrasts with the international empirical literature documenting its negative impact on carbon footprints (Magazzino et al., 2022; Popp, 2019). This apparent divergence can be explained by the incipient, fragmented, and non-substitutive nature of the energy transition in ALADI countries, where renewable energy coexists with a high dependence on fossil fuels. In this context, the expansion of clean energy does not necessarily displace polluting sources, thereby limiting its environmental impact. Several studies have highlighted that the effectiveness of renewable energy depends on its ability to replace fossil fuels, rather than merely complement them (Sadorsky, 2009; Apergis and Payne, 2014). In line with ecological modernization theory, sustained emission reductions require not only the adoption of renewable sources but also a comprehensive transformation of the energy matrix and production systems (Mol and Spaargaren, 2000).

Similarly, financial development does not exhibit a significant effect in the estimated model, suggesting that financial deepening has not translated into emission reductions. This result contrasts with the literature that emphasizes the role of financial systems in channeling resources toward sustainable investments and clean technologies (Ozili, 2022; Ahmed et al., 2022). However, it is also consistent with studies indicating that, in developing economies, credit allocation often remains concentrated in traditional, extractive, and carbon-intensive sectors (Levine, 1997; Ojeyinka and Oje, 2024). In this sense (Zhang and Vigne, 2021), the environmental impact of financial development appears to depend critically on institutional quality and the orientation of public policies.

A deeper interpretation of these findings suggests that the relationship between financial development, industrialization, and CO2 emissions operates through indirect structural mechanisms rather than through isolated direct effects. In particular, financial development may influence environmental outcomes by facilitating capital allocation toward industrial expansion, thereby increasing energy demand and carbon emissions. This case implies the existence of a transmission channel in which industrialization acts as an intermediate mechanism linking finance to environmental pressure.

From this perspective, the absence of a statistically significant direct effect of financial development in the dynamic specification does not necessarily indicate irrelevance; rather, it suggests that the productive structure may mediate its impact. In economies characterized by carbon-intensive industrialization, financial deepening can indirectly reinforce emissions-intensive activities by supporting sectors with high energy requirements. Conversely, in contexts where financial resources are directed toward cleaner production processes, this channel may contribute to emission reduction.

Although the present study does not explicitly estimate a mediation or interaction framework, the empirical findings are consistent with a conditional relationship in which the environmental impact of financial development depends on the level and composition of industrialization. This interpretation aligns with recent literature that emphasizes the contingent effects of finance on environmental sustainability. Therefore, future research could extend this analysis by incorporating interaction terms between financial development and industrialization, or by testing mediation mechanisms to better capture these indirect effects.

In contrast, economic growth shows a positive, marginally significant effect on CO2 emissions, suggesting that economic expansion remains associated with higher pollution levels. This finding is consistent with political economy approaches to carbon emissions, which argue that, in emerging economies, growth remains dependent on energy- and resource-intensive productive structures (Newell and Paterson, 2010; Newell, 2019). Furthermore, these results call into question the empirical validity of the decoupling hypothesis in the region, suggesting that the analyzed countries have not yet effectively separated economic growth from environmental degradation (Wiedenhofer et al., 2020). This outcome is also aligned with studies documenting persistent positive relationships between growth and emissions in early stages of development (Grossman and Krueger, 1995).

Regarding trade openness, the results reveal a positive, statistically significant effect on emissions, reinforcing the notion that international integration may contribute to environmental degradation in peripheral economies. This finding is consistent with dependency theory, which posits that integration into global markets tends to reinforce specialization patterns in resource- and energy-intensive sectors (Bértola and Ocampo, 2014; Svampa, 2019). Although the literature also acknowledges the potential of trade to facilitate technological transfer (Grossman and Krueger, 1995), the evidence suggests that, in the case of ALADI, the scale effect—associated with the expansion of polluting productive activities—predominates (Le et al., 2016). This pattern has been widely documented in developing economies (Antweiler et al., 2001).

Finally, industrialization emerges as one of the most robust determinants of emissions, exhibiting a positive and highly significant effect. This result confirms that productive structure plays a central role in shaping environmental outcomes. In line with the political economy of development, emissions should not be understood solely as an environmental issue, but rather as the manifestation of an accumulation model based on carbon-intensive sectors (Jessop, 2015; Leach et al., 2015). In this context, industrialization—absent a sustainable technological transition—remains a key driver of environmental pressures (Le et al., 2016).

From a policy perspective, the empirical findings suggest that environmental sustainability in ALADI countries requires more targeted and structurally oriented interventions. In particular, the strong, statistically significant role of industrialization underscores the need for industrial policy reforms to promote green industrialization. This study includes adopting cleaner production technologies, improving energy efficiency, and gradually reorienting productive structures toward less carbon-intensive sectors.

In addition, the positive effect of trade openness on emissions indicates that trade policies should be aligned with environmental objectives. This study may involve encouraging export diversification toward low-emission industries, strengthening environmental standards within global value chains, and discouraging specialization in carbon-intensive sectors.

Furthermore, the positive association between economic growth and CO2 emissions suggests that current growth models remain environmentally unsustainable. Therefore, policy efforts should focus on fostering decoupling by investing in renewable energy, sustainable infrastructure, and innovation systems oriented toward environmental performance.

Overall, these findings underscore that achieving a successful green transition in the region requires coordinated policy frameworks that simultaneously address industrial structure, trade integration, and energy systems, rather than relying solely on technological progress or financial expansion.

5 Conclusions

The analysis undertaken provides a comprehensive understanding that environmental challenges in ALADI countries are deeply rooted in the historical configuration of their economic structures. Beyond recent changes or isolated efforts, the organization of production, the nature of predominant economic activities, and patterns of energy use remain decisive factors shaping environmental pressure. This case suggests that advancing toward a more sustainable model requires transformations that go well beyond incremental or short-term adjustments.

In this context, technological progress—while essential for economic development—does not necessarily translate into environmental improvements unless it is explicitly oriented toward that objective. The adoption of new technologies may even reinforce existing dynamics when its primary purpose is to expand production or enhance economic efficiency without accounting for environmental impacts. Therefore, the challenge lies not merely in fostering innovation, but in defining how and for what purpose innovation is pursued.

Similarly, access to financial resources and the development of credit systems do not, in themselves, guarantee a transition toward more sustainable practices. The allocation of these resources is a critical factor, as it may either reinforce traditional, environmentally intensive activities or support cleaner production processes. This case underscores the importance of aligning economic decision-making with objectives that integrate growth and sustainability.

In the energy sector, the observed changes reflect progress that has yet to alter the underlying structure of energy consumption substantially. The coexistence of different energy sources indicates that the transition is neither immediate nor uniform, and that structural constraints continue to limit the impact of cleaner alternatives. This case highlights the need for deeper transformations in both energy production and consumption patterns.

Moreover, the relationship between economic activity and environmental pressure remains strong and persistent. This case suggests that prevailing growth models remain grounded in resource-intensive dynamics, thereby constraining progress toward a balance between economic development and environmental sustainability. In this sense, the central challenge is not merely to achieve growth, but to do so under fundamentally different conditions.

Finally, the manner in which countries integrate into international trade significantly influences the characteristics of their production systems and, consequently, their environmental impact. From a policy perspective, this implies the need to strategically align trade integration with sustainability objectives by promoting export diversification toward less carbon-intensive sectors and strengthening environmental standards within global value chains. In addition, the central role of industrialization identified in this study underscores the importance of advancing green industrial policies that focus on cleaner production, technological upgrading, and structural transformation.

Moreover, the persistence of a positive relationship between economic growth and emissions suggests that current development models remain environmentally unsustainable. Therefore, achieving a sustainable transition requires coordinated policy frameworks that simultaneously address industrial structure, trade dynamics, and energy systems, fostering a gradual decoupling between economic growth and environmental degradation.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frevc.2026.1825864/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Yifan Shen, Tongji University, China

Reviewed by: Daniel Francisco Nagao Menezes, Faculdades Campinas, Brazil

Awais Ur Rehman, University of Malaysia Sarawak, Malaysia

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. The Article Processing Charge (APC) was funded by Universidad de Las Américas (Ecuador).

Data availability

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

XM-U: Investigation, Methodology, Writing – original draft, Conceptualization, Software, Writing – review & editing, Formal analysis, Validation, Visualization. AN-N: Formal analysis, Investigation, Software, Data curation, Writing – review & editing, Writing – original draft, Validation, Conceptualization, Methodology, Visualization, Supervision. MS: Writing – review & editing, Validation, Formal analysis, Writing – original draft, Data curation, Visualization, Conceptualization, Methodology, Investigation. JN-G: Writing – original draft, Investigation, Writing – review & editing, Methodology, Validation, Conceptualization. PA-V: Project administration, Writing – original draft, Supervision, Data curation, Formal analysis, Methodology, Funding acquisition, Conceptualization, Investigation, Writing – review & editing, Resources.

Acknowledgements

The authors would like to thank the Dirección de Investigacióny Desarrollo-DIDE of the Universidad Técnica de Ambato. Thisarticle is derived from the research project entitled “Aplicación weby movil para el cálculo de la huella ecológica como herramientade responsabilidad social empresarial en empresas de EconomíaPopular y Solidaria: Un enfoque desde la equidad de géneroen la provincia de Tungurahua,” approved with Resolution No. UTA-CONIN-2026-0070-R by the Dirección de Investigación y Desarrollo-DIDE of the Universidad Técnica de Ambato, Ecuador. The authors acknowledge that this research has received financial support for publication from the University of the Americas-Ecuador as part of internal research project 518.A.XV.24.

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Citation

Morales-Urrutia, X., et al. (2026). Green transition and productive structure in Latin America: the role of innovation, finance, and the energy matrix in ALADI countries. Frontiers in Environmental Economics, Volume 5. https://doi.org/
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Copyright © 2026 Morales-Urrutia, Núñez-Naranjo, Solórzano, Naranjo-Gaibor and Acosta-Vargas.

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*Correspondence: Patricia Acosta-Vargas

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