The concept of green governance has emerged as a critical mechanism for addressing global environmental challenges, particularly in facilitating the transition toward a low-carbon economy. Green governance emphasizes participatory, transparent, and accountable environmental decision-making processes (Dai et al., 2025; Debbarma and Choi, 2022; Radtke, 2025). Scholars have increasingly underscored the necessity of robust governance structures to support climate policy implementation and sustainable natural resource management (He Q. et al., 2024). In parallel, the notion of institutional resilience—defined as the capacity of institutions to absorb disturbances, adapt to change, and maintain essential functions—has gained attention for its role in ensuring long-term environmental policy effectiveness (Chen et al., 2024).

The transition to a low-carbon economy requires both effective environmental governance and strong institutional resilience. Green governance involves policies and practices that promote sustainable development, while institutional resilience refers to an institution's ability to adapt and respond to environmental change. A study by Bambi et al. (2024) highlights that good governance and high institutional quality significantly reduce the ecological footprint in Sub-Saharan African countries. Their findings show that a 1% improvement in institutional quality can reduce the per capita ecological footprint by 0.0171, emphasizing the crucial role of governance in improving environmental outcomes. Using the Country Policy and Institutional Assessment (CPIA) index to assess institutional quality, the study demonstrates that effective governance can mitigate environmental degradation.

Similarly, research by Yuning et al. (2024) emphasizes that institutional pressures play a key role in driving low-carbon innovation policies. However, the adaptation of these policies is often constrained by diverse governance structures and differing environmental interpretations among organizations. Other studies highlight the importance of Environmental Management Systems (EMS) as mediators translating institutional pressures into tangible innovations (Nguyen, 2022). Kassouri and Altintaş (2020) explored how good governance influences environmental quality, revealing that strong regulatory systems and active participation of green political parties are essential for improving environmental outcomes (Adekunle, 2021).

In the context of low-carbon tourism in rural China, Guo and Li (2025) identified that the implementation of effective policies requires an adaptive, multi-level governance approach. Their study shows that policy success depends on the interaction among national policies, provincial adoption, and local implementation, taking into account economic, environmental, and institutional factors. Additionally, institutional pressures such as government regulations and societal expectations encourage businesses to adopt green innovations. Achieving a low-carbon economy requires not only technical policy innovation but also a philosophical foundation for public service that values responsibility, justice, and long-term stewardship (Tao, 2020). However, effectiveness is influenced by corporate environmental identity and mimetic pressures, indicating that internal organizational factors also play a significant role (Ding and Wang, 2025; Gao et al., 2025; Huang et al., 2022; Purnomo et al., 2024).

Other studies show that among the twelve largest emitting countries, green energy consumption and governance quality significantly reduce CO₂ emissions. However, green innovation does not have a significant effect, underscoring the importance of investment in green energy and improved governance to address environmental challenges (Khan, 2025). Research by Gao et al. (2025) in China demonstrates that low-carbon city pilot policies (LCCPP) significantly enhance corporate green innovation, particularly during the growth and maturity stages. These policies increase financial subsidies and RandD investment, thereby driving technological innovation and supporting the transition to a low-carbon economy. Tengfei and Ullah (2024) found that in OECD countries, market-based policies and pricing mechanisms play a key role in improving green total factor energy efficiency (GTFEE). However, economic growth shows a negative relationship with GTFEE, indicating a trade-off between economic expansion and energy efficiency, and highlighting the need for a global consensus on implementing green growth strategies. In mangrove ecosystems—where socio-ecological complexity intersects with governance challenges—institutional resilience becomes particularly relevant. Mangroves provide critical ecological services, including carbon sequestration, coastal protection, and biodiversity support, yet remain vulnerable to degradation driven by weak governance and fragmented institutional arrangements (Akram et al., 2023; Dabalà et al., 2023; Ferreira et al., 2022).

Recent scholarship continues to explore green governance in the context of climate adaptation and institutional development. For instance, Fuchs et al. (2025) emphasized the role of hybrid institutions in facilitating adaptive management in coastal regions. Similarly, Marks and Pulliat (2022) examined the evolving functions of institutional resilience in decentralized governance systems across Southeast Asia. These studies underscore the relevance of integrating resilience thinking into environmental governance, particularly in areas experiencing increasing ecological stress. In addition, Allen et al. (2023) found that polycentric arrangements and cross-level collaboration enhance institutional responsiveness to climate-related risks. This study builds upon and extends this literature by empirically testing institutional resilience as a mediating factor within a low-carbon governance model, applied in a mangrove ecosystem.

While a growing body of literature addresses the roles of governance in climate policy (e.g., Adekunle, 2021; Altintaş and Kassouri, 2020; Bambi et al., 2024), limited empirical research explicitly examines the mediating role of institutional resilience in the relationship between green governance and environmental policy effectiveness—particularly in mangrove ecosystems, which are both ecologically vital and institutionally complex. Existing studies also tend to focus on national-level policy, without sufficiently addressing how local institutional dynamics affect ecosystem governance and carbon mitigation strategies (Khanam et al., 2025; Ulibarri, 2019; Vallury et al., 2022). This study addresses that gap by empirically analyzing how green governance influences institutional resilience and, in turn, supports the low-carbon transition in the management of mangrove ecosystems in Indonesia.

Much of the existing research relies heavily on qualitative or descriptive approaches. There remains a paucity of studies employing advanced quantitative models such as Structural Equation Modeling (SEM) integrated with MICMAC Analysis to test causal relationships among governance quality, institutional capacity, and policy outcomes (Fathi, 2024; Kanchanawongpaisan, 2024; Lutfi et al., 2024). To address this methodological gap, the present study adopts a mixed-methods approach. It applies SEM to quantitatively examine the mediating role of institutional resilience, complemented by MICMAC prospective analysis, conducted through surveys and Focus Group Discussions (FGDs) with academic experts and stakeholders in environmental governance. This integrative approach enhances the robustness of the findings by combining empirical modeling with participatory foresight, thereby offering both diagnostic and strategic insights into the low-carbon transition in mangrove ecosystem management.

Studies from different regions provide useful points of comparison for positioning the Indonesian case. In Vietnam and Cambodia, mangrove governance is largely centralized under strong state authority (Fidelman et al., 2017; Nguyen, 2022). This centralization ensures more consistent policy enforcement but often limits space for meaningful community participation. Similarly, in Myanmar, state-led frameworks dominate decision-making, leaving little room for local-level initiatives (Lin et al., 2019).

By contrast, in Bangladesh and Brazil, co-management arrangements that combine government regulation with community-based participation have been developed. These hybrid governance models enhance local ownership and adaptive capacity but often face challenges of scaling up and securing long-term political support (Arif, 2022; Favoretto et al., 2024). In Cameroon and Uruguay, institutional adaptation has relied on strong legal frameworks and donor-supported projects, but implementation gaps remain due to weak enforcement capacity (Ajonina, 2022; Owusu et al., 2024).

In East Asia and Southeast Asia more broadly, polycentric governance systems where multiple actors at different levels share overlapping responsibilities have been shown to increase institutional adaptability, though they also create coordination challenges (Lee and Krasny, 2017; Morita and Matsumoto, 2018). In Latin America, comparative studies highlight how governance innovations such as payments for ecosystem services have strengthened institutional resilience, but they require stable political and economic conditions to succeed (Pichancourt et al., 2025; Sattler, 2022).

Against this background, Indonesia represents a distinctive case. Its decentralized governance framework assigns significant authority to local governments under the autonomy system, creating opportunities for participatory approaches to mangrove management. However, decentralization has also led to fragmented responsibilities and overlapping mandates among institutions such as the Ministry of Environment and Forestry, the Ministry of Marine Affairs and Fisheries, and local governments. Unlike more centralized systems (e.g., Vietnam, Myanmar) or hybrid co-management models (e.g., Brazil, Bangladesh), Indonesia's governance challenges lie in ensuring coordination across multiple levels of authority. This makes institutional resilience particularly critical, as it enables governance systems to adapt, coordinate, and sustain effective environmental management amid decentralization and administrative complexity.

Building on this context, the literature highlights that strong green governance and institutional resilience are essential for the effectiveness of environmental policies and for facilitating the transition to a low-carbon economy. Effective policy implementation requires alignment within governance structures, investments in green energy, and adaptive environmental management systems. In Indonesia's case, addressing the challenges of fragmented authority and overlapping mandates through resilient institutions can enhance policy coherence, improve coordination across levels of government, and promote stakeholder engagement. Robust governance mechanisms, combined with investments in renewable energy and green financial support, play key roles in reducing carbon emissions and achieving sustainable development. Integrating green governance principles into environmental policies, while considering socio-economic contexts and involving various stakeholders, can thus strengthen institutional capacity, ensure policy effectiveness, and support Indonesia's transition toward a low-carbon, sustainable future.

This study adopts a mixed-method research design by integrating Structural Equation Modeling (SEM) and Prospective Analysis (MICMAC). The objective is to examine the causal relationships between green governance, institutional resilience, and their influence on the low-carbon transition, while simultaneously identifying strategic variables that shape the future policy landscape. The research methodology follows a systematic and iterative process, as illustrated in Figure 2.

The use of Structural Equation Modeling (SEM) enables a robust analysis of latent variables and mediation effects among governance constructs, whereas MICMAC provides strategic mapping of inter-variable influence and dependency relationships. These methods are complementary: SEM statistically tests the strength and direction of causal paths, while MICMAC offers insight into the systemic structure of variables that drive or depend on green governance outcomes. Their integration thus allows a more comprehensive understanding of both empirical relationships and strategic institutional positioning.

The research methodology framework integrates SEM and MICMAC Prospective Analysis through a sequential process. It begins with a literature review, hypothesis formulation, and model specification. Following data collection, a decision point assesses the adequacy of the data for SEM analysis. If the data meet the statistical requirements, SEM is conducted and subsequently integrated with MICMAC to identify key influencing variables and possible future scenarios. If not, the MICMAC method is applied independently after the measurement model specification. This iterative approach enhances the robustness of the analysis by combining statistical modeling with expert-based foresight analysis.

An extensive literature review was conducted to identify theoretical frameworks and empirical findings related to green governance, institutional resilience, the low-carbon economy, and environmental performance. Based on this review, hypotheses were formulated to examine the relationships among key constructs, including governance quality, policy implementation, stakeholder engagement, and environmental outcomes. Based on the theoretical framework, this study formulates the following hypotheses:

The study was conducted in Bengkalis Regency, located in Riau Province, Indonesia—a coastal area known for its ecological significance and strategic role in mangrove conservation. Bengkalis represents one of the regions with the lowest mangrove growth rates among coastal regencies in Riau Province, making it a critical case for examining governance and institutional resilience in low-carbon development strategies. The area is characterized by high ecological vulnerability resulting from anthropogenic pressures, climate change impacts, and fragmented environmental management frameworks.

The study population consisted of 135 identified experts in mangrove ecosystems, representing three main groups: academics, government officials, and NGO practitioners. A census approach was employed, inviting all identified individuals to participate in the SEM survey. A total of 101 valid responses were obtained, yielding a response rate of 74.8%, which is considered adequate for SEM analysis (Kabit et al., 2020). Since the entire population was targeted, no random sampling procedure was applied; the earlier reference to “randomization” was inaccurate and has been corrected.

For the MICMAC analysis, a smaller expert panel of 10 individuals was convened, selected purposively based on their expertise in mangrove governance and professional experience. This panel was distinct from the SEM respondents to ensure methodological complementarity: the SEM survey captured broad quantitative perspectives, while the MICMAC approach focused on strategic foresight from senior experts. To enhance transparency and replicability, the full SEM questionnaire is provided in Appendix A, and the MICMAC cross-impact matrix instrument in Appendix B.

Primary data were collected through structured surveys and expert interviews. Respondents included policymakers, environmental officers, NGO practitioners, and academic researchers with contextual expertise in green governance and mangrove ecosystem sustainability. Secondary data were obtained from government reports, environmental performance indicators, and academic publications to support data triangulation.

This research adheres to the ethical guidelines of Riau University. Prior to participation, informed consent was obtained from all respondents. Participants were assured of the confidentiality and anonymity of their responses, and participation was entirely voluntary. No personally identifiable information was collected or disclosed.

Before conducting the Structural Equation Modeling (SEM) analysis, a descriptive analysis was carried out to understand respondent characteristics and data distribution. This included descriptive statistics such as mean, standard deviation, frequency distribution, and data visualization, ensuring that the dataset met the basic assumptions required for subsequent statistical analyses.

A stratified random sampling technique was employed to ensure proportional representation of each subgroup within the population, reducing sampling bias and enhancing the validity of the study's findings. Using Slovin's formula with a 5% margin of error, the sample size was determined to be 101 respondents, deemed sufficient to accurately represent the target population. This sampling approach strengthened data reliability, allowing the dataset to be used confidently for validity and hypothesis testing within the SEM framework.

The respondents' distribution reflected diverse professional backgrounds and levels of environmental policy experience. Of the total respondents, 40% were affiliated with government institutions and environmental regulatory bodies, serving as key actors in formulating and implementing green governance policies. Another 25% were academics and researchers, contributing to scientific assessments and evidence-based evaluations. The remaining 35% represented the private sector and industry, functioning as principal implementers of environmental policies in practical contexts.

Regarding experience in environmental policy-making, 30% of respondents had less than 5 years of experience, typically consisting of early-career professionals or those newly engaged in environmental governance. Approximately 45% reported 5 to 10 years of experience, indicating moderate engagement in environmental issues, while 25% possessed over 10 years of experience, reflecting deep expertise in policy development and implementation. This distribution underscores the inclusion of diverse stakeholders with varying experience levels, providing comprehensive insights into green governance, institutional resilience, and environmental policy effectiveness in advancing the low-carbon transition.

Before testing the structural relationships among variables, a Confirmatory Factor Analysis (CFA) was conducted to assess the validity and reliability of the measurement model (Table 1). This step ensured that each indicator accurately represented its respective construct and that the overall model demonstrated strong internal consistency and reliability.

The convergent validity test indicated that all indicators had factor loadings above 0.6, signifying that each strongly represented its respective construct. Furthermore, all Average Variance Extracted (AVE) values exceeded 0.5, confirming that each latent variable explained the majority of variance in its indicators. Thus, the measurement model demonstrated good convergent validity.

Discriminant validity was tested using the Fornell-Larcker criterion (Table 2). The results showed that the correlations between constructs were lower than the square roots of their respective AVE values, confirming that each construct was empirically distinct with minimal overlap. To further assess internal consistency and reliability, Cronbach's Alpha and Composite Reliability (CR) were examined. All constructs yielded Cronbach's Alpha and CR values above 0.7, indicating strong reliability and internal consistency. Based on these findings, the measurement model meets all necessary validity and reliability criteria, making it appropriate for subsequent structural analysis.

After confirming that the measurement model met the validity and reliability requirements, the next stage involved examining the structural relationships among variables using Structural Equation Modeling (SEM) with the support of SmartPLS software. This analysis aimed to evaluate how well the proposed model explains the relationships between green governance, institutional resilience, and environmental policy effectiveness in supporting the transition toward a low-carbon economy.

Table 3 presents the model evaluation based on several fit indices that assess the degree to which the proposed model aligns with the empirical data. The results are interpreted as follows:

Based on these results, the SEM model demonstrates a strong overall fit, confirming its suitability for testing the hypothesized relationships among the study variables.

The results of hypothesis testing using SEM are presented in Table 4.

Table 4 presents the results of the Structural Equation Modeling (SEM), illustrating the relationships between the variables examined in this study. The strength and direction of these relationships are measured through path coefficients (β), as follows:

Figure 3 illustrates the SEM outcomes, providing a comprehensive depiction of how Green Governance (GG) influences the development of a Low-Carbon Economy (LCE) within mangrove ecosystems through both direct and indirect pathways. Green Governance functions as a pivotal exogenous variable, exerting direct positive effects on Institutional Resilience (β = 0.67, t = 7.89, p < 0.001), Environmental Policy Effectiveness (β = 0.61, t = 7.12, p < 0.001), and the Low-Carbon Economy (β = 0.52, t = 6.45, p < 0.001). These findings underscore the fundamental role of governance mechanisms in driving systemic transformation toward sustainability.

Furthermore, Institutional Resilience and Environmental Policy Effectiveness emerge as critical mediating (endogenous) variables. Institutional resilience indirectly influences the Low-Carbon Economy (β = 0.45), supported by a significant Sobel test result (z = 5.98, p < 0.005). Similarly, Environmental Policy Effectiveness mediates the relationship between governance and economic outcomes (β = 0.38; Sobel z = 5.47, p < 0.005). These results highlight that resilient institutions and effective policies act as essential channels through which governance contributes to sustainable environmental and economic outcomes.

The Low-Carbon Economy serves as the ultimate endogenous construct, receiving both direct and indirect effects from the other variables. This demonstrates the interconnected nature of governance, institutional dynamics, and policy effectiveness in achieving sustainable, low-carbon transitions within vulnerable coastal ecosystems. The strength and significance of these relationships confirm the robustness of the proposed theoretical framework and emphasize that enhancing governance quality and institutional resilience are key strategies for advancing low-carbon development in mangrove regions.

The MICMAC (Matrice d'Impacts Croisés Multiplication Appliquée à un Classement) analysis was employed to map the influence and dependency relationships among 24 sub-variables across the four main constructs: Green Governance (GG), Institutional Resilience (IR), Environmental Policy Effectiveness (EPE), and Low-Carbon Economy (LCE). The analysis results are visualized in a four-quadrant matrix that categorizes variables according to their degree of influence and dependency within the overall system.

Figure 4 illustrates the results of the MICMAC analysis, which identifies the driving and influencing factors in Green Governance. The analysis divides the variables into four quadrants, as described below.

The SEM results underscore the pivotal role of Green Governance (GG) in shaping both Institutional Resilience (IR) (β = 0.67) and Environmental Policy Effectiveness (EPE) (β = 0.61), with an indirect pathway to the Low Carbon Economy (LCE). This indicates that governance mechanisms such as public participation (GG2) and institutional coordination (GG3) function as upstream enablers. The MICMAC analysis positions these indicators in Quadrant I (high influence, low dependency), confirming their role as driving variables.

These findings are consistent with previous research indicating that robust green governance practices—such as policy transparency, inclusive stakeholder engagement, and the use of environmental incentives—serve as critical enablers for advancing low-carbon economic strategies (Araujo-Vizuete and Robalino-López, 2025; He G. et al., 2024; Kwilinski et al., 2023; Raihan et al., 2025; Zhang and Chen, 2025). Strengthening policy frameworks that emphasize sustainability, energy efficiency, and climate change mitigation can play a key role in helping industrial and business sectors reduce carbon emissions.

This result highlights that governance is not merely an administrative or regulatory function but a strategic lever for sustainable transformation. Effective green governance ensures stakeholder engagement, accountability, and coordinated policy execution. In contexts where ecological sustainability is at risk, activating participatory and integrative governance frameworks becomes essential. Although this study aligns with Sustainable Development Goals (SDGs) such as SDG 13 (climate action), SDG 14 (life below water), and SDG 16 (strong institutions), our findings suggest a more substantive link: effective mangrove governance supports broader development objectives by enhancing institutional equity, fostering participatory environmental management, and ensuring the equitable distribution of ecological benefits. These outcomes illustrate the embeddedness of ecosystem resilience within sustainable development efforts, especially at the subnational level.

Institutional Resilience (IR) emerges in the SEM model as a mediating variable between GG and LCE, with a significant indirect effect (0.4; Sobel test = 5.98, p < 0.005). The MICMAC findings show that variables such as IR3 (stability) and IR4 (capacity for innovation in environmental strategies) are located in Quadrant II (high influence and high dependency), classifying them as leverage variables.

The relatively high path coefficient between green governance and institutional resilience (β = 0.67, p < 0.001) suggests a strong and significant relationship, underscoring the critical role of institutional frameworks in supporting environmental governance systems. This finding indicates that effective governance practices—such as transparency, stakeholder participation, inter-agency coordination, and long-term policy commitment—can substantially enhance institutional capacity to withstand and adapt to environmental pressures and evolving policy landscapes.

Institutional resilience, in this regard, functions as a mediating mechanism that enables systems to remain stable while being flexible in responding to uncertainties. Resilient institutions facilitate regulatory adaptability, promote learning-based policy reform, and strengthen cross-sectoral cooperation—all of which are essential to address complex sustainability challenges (Ostrom, 2009; Pichancourt et al., 2025). This aligns with prior research emphasizing that institutional robustness and adaptability are fundamental to achieving long-term environmental objectives (Akhtar et al., 2023; Guttman et al., 2018; Yuning et al., 2024).

Moreover, the MICMAC analysis reinforces this interpretation by positioning key institutional elements such as leadership commitment and stability in the quadrant of high influence and high dependency. This dual role underscores the systemic leverage potential of institutional resilience: it is shaped by governance practices yet exerts critical influence on downstream outcomes such as environmental policy effectiveness and the low-carbon economy. Therefore, fostering institutional resilience requires targeted investments in leadership development, regulatory innovation, and institutional learning frameworks.

Ultimately, the effectiveness of green governance initiatives is contingent upon the capacity of institutions to remain responsive and adaptive. In rapidly evolving environmental and socio-political contexts, policy implementation cannot be static; it must be supported by institutions capable of iterative learning, multi-actor engagement, and dynamic strategy adjustment (Johnson et al., 2021; Partidário et al., 2023). Strengthening institutional resilience is thus not merely a technical necessity but a strategic imperative in the transition toward sustainable and low-carbon development.

The empirical findings reveal that green governance (GG) exerts a significant positive influence on environmental policy effectiveness (EPE) (β = 0.61, p < 0.01), supporting prior studies linking governance quality to environmental performance (Dincă et al., 2022; Najia et al., 2024; Wang et al., 2023). This suggests that governance frameworks embedding transparency, inclusiveness, stakeholder engagement, and accountability enhance both institutional legitimacy and policy implementation effectiveness.

Furthermore, the study identifies EPE as a partial mediator in the relationship between green governance and low-carbon economic transformation, with an indirect effect size of 0.3 (Sobel test = 5.47, p < 0.005). This mediation implies that governance mechanisms exert much of their transformative influence through the design, enforcement, and responsiveness of environmental policy instruments. The significance of EPE as an intermediary variable underscores that policy quality is a key channel through which governance is translated into tangible climate and sustainability outcomes.

Complementing the structural model, the MICMAC analysis further highlights the strategic positioning of two EPE-related variables—EPE4 (supervision) and EPE5 (economic instruments)—both categorized in Quadrant II, indicating high influence and low dependency. These findings suggest that supervision mechanisms (e.g., compliance audits, environmental monitoring) and market-based instruments (e.g., green subsidies, environmental taxes) are pivotal leverage points. Their relatively autonomous position indicates that targeted enhancements in these domains can produce ripple effects across the governance system without requiring heavy reliance on upstream variables.

The alignment between SEM and MICMAC thus provides analytical convergence, emphasizing that environmental policy effectiveness is not merely an outcome of governance but a strategically situated subsystem capable of driving systemic transitions when properly empowered. As the literature highlights, leveraging economic instruments and ensuring robust oversight mechanisms are essential for achieving long-term decarbonization targets and fostering adaptive environmental institutions (Ali, 2024; Liu, 2023; Madadizadeh et al., 2024; Shah et al., 2022; Wu and Tham, 2023).

Accordingly, strengthening EPE—particularly through improved supervision and economic instruments—can act as a catalytic mechanism for accelerating the transition toward a low-carbon economy. These efforts must be embedded within a broader governance strategy that promotes institutional coherence, enhances participatory processes, and fosters regulatory innovation.

As a result, the Low Carbon Economy (LCE) is the final dependent variable in the SEM framework, significantly influenced by IR and EPE. The MICMAC results confirm this, placing most LCE indicators in Quadrant III (high dependency, low influence), marking them as output variables. This configuration reflects the nature of the LCE as a systemic outcome dependent on the upstream performance of governance structures, institutional quality, and policy effectiveness. It also indicates that interventions targeting LCE outcomes must be preceded by strong institutional and governance foundations. Policy pathways toward a low-carbon economy should therefore be designed using an ecosystemic logic that integrates governance, regulation, participation, and institutional reform.

This study reveals that institutional resilience serves as a significant mediating factor between green governance and the low-carbon economy (β = 0.45, p < 0.05). Institutional resilience plays a pivotal role in sustaining green policy implementation and enhancing the preparedness of both public and private sectors in responding to environmental challenges. In this regard, adaptive regulatory frameworks and robust institutional support are essential to facilitate the transition toward a low-carbon economy (Green and Gambhir, 2020; Hall et al., 2017).

Moreover, environmental policy effectiveness also emerges as a key determinant of low-carbon economic outcomes (β = 0.38, p < 0.05). Strong environmental regulations can incentivize investment in renewable energy, resource efficiency, and sustainable production practices. These findings align with previous studies emphasizing the central role of environmental policy in fostering sustainable business ecosystems and promoting green innovation (Ullah et al., 2025; Naruetharadhol et al., 2024). Similarly, Rahman and Islam (2024) and Setyadi et al. (2025) highlighted the mediating influence of institutional capacity in coastal policy outcomes. However, unlike their studies, this research incorporates strategic variable mapping through MICMAC to provide a more dynamic view of institutional interdependencies. Additionally, our results differ from Owusu et al. (2024), who found no significant mediation effect of resilience in lowland forest governance, suggesting that ecosystem type and governance structure play a critical role in shaping institutional function.

By combining SEM and MICMAC, this study offers a novel empirical contribution to the literature on green governance—particularly in fragile coastal ecosystems where institutional dynamics are highly complex. The integration of institutional resilience within the low-carbon policy framework provides new insights into how adaptive governance models can be developed to address localized environmental challenges.

This study contributes to the literature not only through its methodological innovation but also through its theoretical, contextual, and practical insights. From a theoretical standpoint, the research advances the discourse on environmental governance by empirically demonstrating the mediating role of institutional resilience in linking green governance to environmental policy effectiveness and the transition toward a low-carbon economy. Whereas, previous studies have typically examined these constructs in isolation, this study integrates them into a unified explanatory framework, thereby extending governance and institutional theories within socio-ecological systems.

From a methodological perspective, novelty arises from the combined application of Structural Equation Modeling (SEM) and MICMAC analysis. SEM offers robust statistical validation of causal linkages among governance, resilience, policy effectiveness, and low-carbon transition, while MICMAC provides a strategic mapping of variable interdependencies. The integration of these two methods bridges the gap between quantitative validation and strategic foresight, resulting in a more holistic framework for evidence-based policy formulation.

In terms of contextual contribution, the research focuses on mangrove ecosystems in Indonesia, which host the largest expanse of mangroves globally yet face severe degradation pressures. Unlike most previous studies conducted in developed contexts or at the national level, this study highlights a Global South perspective at the local governance scale. This lens underscores the governance challenges of reconciling ecological preservation with socio-economic needs in coastal regions, generating insights that are both locally grounded and globally resonant.

Finally, the study makes a practical contribution by identifying actionable priorities for policymakers. The integration of SEM and MICMAC results points to public participation, regulatory enforcement, and economic instruments as key leverage factors for institutional strengthening. This dual methodological and strategic orientation ensures that the findings are both academically rigorous and policy-relevant, providing concrete pathways for enhancing green governance and institutional resilience in the pursuit of a low-carbon economy.

The findings hold several policy implications. First, enhancing institutional resilience is critical to improving environmental policy effectiveness in mangrove ecosystems. This can be achieved through capacity-building, decentralized decision-making, and participatory planning that empowers local communities. Second, governance drivers such as cross-sectoral coordination and regulatory enforcement should be prioritized in policy reform agendas. Nonetheless, this study has limitations. The empirical data were collected from a single regency, which may constrain the generalizability of findings to broader national or regional contexts. Moreover, while SEM and MICMAC provide strong analytical capabilities, their outputs depend on the accuracy of input variables and expert assessments, which may introduce interpretive bias.