Socio-technological risks are risks of global catastrophic risks including diseases epidemics, adverse climate change, and technologies’ failure among others [1]. Such risks are not always restricted by a specific country or state, and hence they demand a systemic as well as an integrated approach in order to avoid the worst-case scenarios. Effective management of these risks is predicated on three critical dimensions: such as governance effectiveness, the identification of risks and their probable preventive measures [2]. Despite such efforts, there are still some gaps in identifying the relationship between various dimensions of such risks and possible results [3].

There is always a significant focus on the governance role in the co-ordination process to undertake measures easing vulnerabilities and improving on resilience [4]. The provision of effective gurus that will define policies for developing and executing procedures that meet the organization goals in relation to the present and future threats that are posed to the organization [5]. Nevertheless, variation regarding governance effectiveness indicates the disparities of catastrophic risk management within regions. Eradicating these disparities can only be achieved by sound analytical tools that could be used to assess governance practices and measure the gaps.

Risk identification is the other central concept in risk management process [6, 7]. It helps to focus on what you need to do and helps to distribute resources appropriately, if potential threats are considered and understood. This is due to the fact that it entails collection of information, identification of risks, determination of potential outcomes that makes the planning of measures to be taken unavoidable. This research established that risk identification is an essential process, but it does not afford clear directions on the process, which results in proliferation of risk identification methodologies in organizations.

Of the three dimensions of governance, mitigation strategies involves implementing measures developed from the policies of governance and the insights from risk identification to minimize or eliminate risks. These are technological; capacity-building; and policy-making strategies. Risk mitigation measures can only be efficient if used in concert with governance goals and objectives, and further, depending on how well the risks are identified in the first place.

While these dimensions are valuable on their own, the relation between them frequently remains in the background [8]. By analysing the relationships between the concepts of governance, risk identification and risk mitigation it would be possible to obtain a broader view on risk management in the context of global catastrophic threats. For instance, the robustness of governance can increase risk detection activities, which in advanced can lead to better ways of managing those risks. On the other hand, weak governance may negatively influence converting the identified risks into policy measures and may even enhance vulnerabilities.

To this end, this study seeks to fill the existing literature gaps by analyzing the interconnectivity of governance effectiveness, risk identification, and risk mitigation processes. It uses Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) in its strong applied analysis to work through all these dimensions. The study assumes that governance effectiveness explains risk identification and mitigation strategies either directly or through other variables.

Through these relationships, the study intends to present useful information to formulators and other players in the policy arena. Promoting better governance, better risk identification, and more effective risk management are essential counter measures against global catastrophic risks. Further, the findings are intended to provide a multi-dimensional framework that can help to narrow regional discrepancies and can be implemented in different socio-political environments.

Based on the fact that global interconnection is on the rise and the scale of catastrophic risks is growing, this work underlines the relevance of risk management based on systemic and preventive approach. In doing so, it makes a theoretical and practical contribution towards the complex debates in the academic literature and development practice as a whole on how to construct more resilient societies in the context of globalization.

Schweizer and Renn (2019) [9]  focus on the management of systemic risks regarding disaster risk reduction and find out that such risks are intertwined in the global catastrophic ones like climate change and pandemics. They posit that centralized structure of conventional governance arrangements are actually cut off and insufficient in handling such threats. They propose a complex, inclusive, and across-organizational system where different actors of different governance levels and from different sectors. Disaster adaptive governance that can accommodate the dynamic risk environment in a community is crucial if the society is to develop the needed resilience required of it to mitigate the effects of disasters.

In their paper Mitra and Shaw (2023) [10] explored current research on systemic risk in disaster management emphasizing that risk management frameworks are inadequate to respond to related and interconnected risks. Such systems demand harmonisation so as they take into consideration interconnections between various sectors for instance, infrastructure, economy, and the environment. Their ideas point to a vast involvement of various stakeholders like the local communities in risk assessment as well as risk management.

Renn (2017) [11] deals with risk governance when there is still uncertainty in a system. He underlines principles that concern the complex integration of scientific evidence with socio-political decisions about governance that are comprehensive and responsive. Criticizing the current risk management approaches for their lack of emphasis on uncertainty, Renn underlines the importance of involving all stakeholders to improve the practical action to address identified threats.

Senevirathne et al. (2023) [12] have adopted the example of the disaster which Japan faced in 2011 and they point out the significance of understanding interconnectedness of various forms of risk. They point out that efforts to make the population safer through disaster risk reduction and resilience are equally important since multiple disasters often occur simultaneously.

Ammann (2006) [13]  does this when he first presents the concept of integral risk management. He underscores the necessity for adaptive and compositive ways of managing global catastrophic risks.

Research Gap

Significant progress has been made in the area of international risk management; however, the relationships between governance effectiveness, risk identification and subsequent risk management strategies remain under examined [14, 15]. The limitation with the existing literature is that many of them Work separately with the dimensions rather than recognising their connectedness. Furthermore, the scope and practices of governance and risk management are still disjointed on regional levels, and policymakers have few tools to learn from other areas on the issue. Current models also fail to provide adequate mediation and moderation analysis as to its indirect effects and potential implications for policies. This research fills these gaps by using a systemic model that encapsulates governance, risk/opportunity search, and management techniques into one framework.

Conceptual Framework

The conceptual framework of this study is based on the assumption that the ability to govern and manage catastrophic risk, the process of risk identification and the measures for risk management are closely related dimensions of catastrophic risk. In other words, governance defines the structural framework within which risks are identified and the way in which these risks are managed. Risk identification also plays the role of intermediary between the company’s governance policies and the creation of useful knowledge. It needs to be pointed out here that mitigation measures are tools orientated towards putting into practice the concepts of governance and risk management with a view of minimising risks. This framework posits direct and indirect interaction between these dimensions with governance being the key exogenous variable. The relationships in these models were tested using Structural Equation Modeling (SEM) which provides an effective evaluation of their interactions.

Hypotheses

• H1: Governance effectiveness has a positive direct effect on mitigation strategies.
• H2: Governance effectiveness has a positive direct effect on risk identification.
• H3: Risk identification mediates the relationship between governance effectiveness and mitigation strategies.
• H4: Regional disparities do not significantly moderate the relationships among governance, risk identification, and mitigation strategies.

These hypotheses aim to unravel the complex dynamics of global catastrophic risk management and provide actionable insights for improving resilience and preparedness.

This paper used a sound quantitative research method to analyse the complex interconnection between the effectiveness of governance, the identification of risks, risk mitigation and global catastrophic risk management interventions. SEM was adopted as the major analysis tool because of its capacity to estimate relationships between variables that are both latent and observed at the same time, this is advantageous in measuring multidimensional concepts such as global risk management.

Data Collection and Sampling

Information was obtained through questionnaires filled in by policymakers, disaster response practitioners, and scholars in the field of risk management. Participants were chosen based on the stratified random sampling so as to include different geographical areas of the country. Such a sampling technique was crucial for the purpose of comparing and contrasting the differences and similarities that exist in the regional approaches to catastrophic risk management.

The CFA method was used to establish the validity of the measurement model by assessing whether the available indicators were a good proxy of the theoretical constructs. CFA was selected because it allows testing purported associations between measurement variables and data latent structures that include variables such as governance effectiveness and mitigators. These measures were crucial for establishing construct validity and reliability which are essential in determining subsequent structural relationships.

CFA and SEM analysis were done using the SPSS AMOS software. AMOS is chosen because in this software, the result includes easy understanding interface, standardized factor loadings, model fit indices and picture of hypothesized model which is important in evaluating whether the model is adequate and adequate enough or not.

Structural Modeling (SEM)

SEM was used to analyze direct and indirect links between governance, risk and appropriate measures. The mentioned technique was chosen in order to conduct the evaluation of mediation effects which is critical to the identification of risk identification as a mediation between governance and mitigation strategies. Another advantage of SEM is that different dependent and independent variables can be analyzed at once, and thus can give an evaluative insight into them.

SEM was conducted using the Mplus software because it is powerful and suitable for missing data and bootstrapping, both of which increase the reliability of mediation analysis. Indirect effects regarding parameter estimates were also made reliable through bootstrapped confidence intervals. Thus, to assess the appropriateness of the structural model and to find further sources of improvement residual analysis was made. Additional, the effect of governance improvements on the risk reduction was established using sensitivity analysis thus policy implications. These methods proved to be very useful as a way of evaluating the robustness of the model to various conditions and conditions.

The use of tools like STATA in residual and sensitivity was due to the efficiency of the statistical models and handling of big data. The sensitivity analysis options available in STATA gave straightforward results on the amount of risk that can be prevented by intervening.

The use of correlation analysis was employed to identify needed correlations that may relate, for example, between governance effectiveness and risk identification. A mediation analysis was also performed and through bootstrapping, further understanding of the indirect effects of governance on mitigation strategies through better identification of risk was gained. These methods were employed to break the causal entanglements and unearth the channels through which governance influences risk management results.

Not only did these analyses support the hypothesized relationships formulated in this research but also offer practical implications to policymakers and disaster response planners. These ways to do CFA, SEM, residual, and sensitivity analyses made the study guarantee the overall assessment of the dynamics related to GCR management around the world by applying the modern statistical methods that would help to find out significant results.

Descriptive Statistics

The first step in the analysis process was the descriptive statistics on the observed variables on the model being investigated. Table 1 provides a summary of the means, standard deviations, and ranges of the variables, which were categorized into three dimensions: risk management including risk identification, risk control and risk management strategies. The results suggest large standard deviations around the mean and a mean score of 3.45 out of five for the perceived effectiveness of governance strategies.

Table 1: Descriptive Statistics of Key Variables in Global Catastrophic Risk Management

Confirmatory Factor Analysis

The Confirmatory Factor Analysis (CFA), which measures the overall appropriateness of the proposed research mode, highly confirmed, with the factor loadings of all indicators above the standard threshold. It is shown in Figure 1 below the hypothesized CFA model and in Table 2 below is the standardized factor loadings. Hence, all loadings were above the threshold of 0.70 indicating good convergent validity.

Figure 1: Hypothesized Structural Equation Model for Global Catastrophic Risk Management

Table 2: Factor Loadings for Latent Variables

Model Fit and Path Analysis

The structural model demonstrated excellent fit: χ²(150) = 180.23, RMSEA = 0.035, CFI = 0.96, TLI = 0.95, and SRMR = 0.04 (Table 3). These indices confirm that the hypothesized model adequately represents the data.

Table 3: Model Fit Indices for SEM

Figure 2 shows the final structural model, including path coefficients. Significant relationships were observed between risk identification and mitigation strategies (β = 0.48, p < 0.01) and between governance effectiveness and overall risk management outcomes (β = 0.61, p < 0.01).

Figure 2: Final Structural Equation Model with Path Coefficients

Mediation and Moderation Effects

Table 4 shows the mediation effects Results. Risk identification played a great role in moderating the relationship between governance effectiveness and mitigation strategies (indirect effect = 0.29, p < 0.01). As expected, there were no moderation effects on geographical region.

Table 4: Mediation and Moderation Effects Analysis

Sensitivity Analysis

Figure 3 captures a sensitivity analysis of mitigation strategies. The simulation results further showed that investment on early warning systems could decrease the overall risk score by 15%.

Figure 3: Sensitivity Analysis of Risk Mitigation Strategies

Residuals and Model Improvement

The evaluation of the SR and MI indicated that there were relatively few areas within the model that could be refined further (Figure 4). The highest residual of 0.25 can be found in the relationship between mitigation strategies and the effectiveness of governance indicating that the current model is able to reflect these relations without distortions.

Figure 4: Standardized Residuals (SR) and Modification Indices (MI)

Advanced Mediation Analysis

Table 5 follows up on the mediation analysis and displays the bootstrapped confidence intervals for indirect effects. These robust results further establish the mediation paths, with all of the confidence intervals not being equal to zero.

Table 5: Bootstrapped Indirect Effects with Confidence Intervals

Scenario Analysis

A scenario analysis was conducted to evaluate the potential impact of increased investment in governance effectiveness. Figure 5 shows that a 10% improvement in governance metrics correlates with a 20% reduction in catastrophic risk scores.

Figure 5: Scenario Analysis of Governance Improvements

Data Analysis and Interpretation

Descriptive Statistics

The primary data analysis included providing a description of the key variables which was achieved by calculating percentages (Table 1). The value of Governance effectiveness was above the mid line at 3.45 (SD = 0.89); suggesting that there was moderate effectiveness of governance in the different regions. The mean value of the subscale risk identification and risk mitigation strategies mean value 4.12 and 3.85 show higher mean means the perceived importance of the strategies to manage risks in general and global catastrophic risks in particular.

Descriptive data for the measures of governance effectiveness imply the probable differences between the regions of analysis, which was advanced with box and whisker plots (Figure 2). Finally, the box plot indicated that the level of variability was highest in Region B; this could be attributed to implementation of the policies.

Confirmatory Factor Analysis

CFA attested to measurement model. In the case of convergent validity, all the standardized factors loadings were above the recommended minimum of 0.70 as it is shown in Table 2 below. For instance, Risk_1 is explained by the factor with the loading of 0.82, while Risk_2 – 0.89, thus highlighting high association of the expression with the latent factor, risk identification.

The hypothesized SEM model with variances and covariances among the latent variables and their links to the indicators are shown in figure 1. Such findings reassured the measurement model validity and allowed proceeding to structural analysis.

Model Fit and Structural Path Analysis

The results of this model fit and the structural path analysis of the four models are presented in Tables 5 and 6 below.

The SEM model had good fit indices within the data (Table 3). The quantitative fit of the identified model was evaluated by the RMSEA of 0.035, and CFI of 0.96 suggesting a reasonable fit between the students’ ideas and the obtained data.

Schematic description of the structural model and estimation results of path analysis are provided in Figure 2. An analysis of the SEM results presented in Figure 5 showed that governance effectiveness was a significant and positive antecedent of mitigation strategies (β = 0.61; p < 0.01). Likewise, risk identification had a large and significant effect on the overall risk management outcomes (β = 0.48, p < 0.01). These results support the argument that governance and proactive identification are critical components in risk management.

Testing of Residuals and Model Diagnostics

When searching for the potential areas of model improvement, the values of SRs, and MI were compared. This figure also shows an indication that the greatest residual of 0.25 was recorded between the mitigation strategies and the governance effectiveness. This implies that other factors that have a bearing on the studied variables and the association between them remain unidentified and should be examined in future research works.

Mediator and Moderator Variables

Table 4 shows the mediating role played by risk identification on the correlation that exists between governance effectiveness and mitigation strategies. The capitalised coefficients indicate that the hypothesized indirect effect of improved risk identification on mitigation strategies is valid with a value of 0.29 (p < 0.01).

These results suggest that there are no significant moderation effects for geographical region, which implies that the relationship presented in the current model under study was invariant across regions.

Reaction and Stress Test

Using sensitivity analysis, governance investments were examined in terms of the effect they have on risk (Figure 5). Counter-intuitively, a 10% gain in the governance effectiveness was tied to a 20% average decline in the integrated catastrophic risk rating. This analysis enables me to argue effectively that there is significant possibility for meaningful risk management and that governance reforms should be a part of it.

By bootstrapping the confidence intervals in Table 5, the mediation was also validated for robust interpretation. For example, regarding the indirect effect of the independent variable, the 95% confidence interval was 0.15–0.43, which constituted evidence of statistical significance.

Correlation Patterns

In Figure 3 trends in the correlations between variables were illustrated where governance effectiveness and risk identification had an obvious positive correlation coefficient of 0.72. This acts as a confirmation of the fact that the governance policies need to be harmonized with appropriate identification risk frameworks so as to enhance successes.

The results of the data analysis outlined the importance of addressing the question of governance and risk in confronting global catastrophic risks. These relationships became easy to understand based on the SEM framework used, which supported statistical validation in addition to good visualization.

Hypotheses test reveals that governance effectiveness exerts the most direct impact on risk identification and mitigation strategy with indirect effect through mediators. The results support the hypothesis that good governance fortifies the recognition of risks, and thus fortifies the execution of sound management strategies. These results indicate that while regional disparities may not moderate these relationships, the proposed framework might apply universally.

Figure 6: Conceptual representation of the new model for Global Catastrophic Risk Management, based on the key results

These understanding stress the centrality of governance in making and implementing robust integrated threat risk management approaches for global catastrophic hazards.

Limitations of the Study

However, some limitations must be taken into consideration Based on the work indicated above some limitation could be pointed out. First, the data was cross-sectional and many of the measures which were taken to reflect the perceived governance effectiveness were self-report. Second, due to cross-sectional design no concurrent relations were possible to establish between variables. Finally, the regional diversity in the study can be a weakness due to the specific context variation in all these regions. These limitations provide rationale for future longitudinal research as well as the collection of more specific information.

Implications of the Study

The research has applied and theoretical value in the following ways: It underlines the need to improve the governance within an organization as well as underlines governance as a fundamental element of managing risks. The findings provide specific recommendations for policymakers in terms of what aspects of governance and risk identification should receive attention and funding. The conceptual framework also helps to advance the academic literature by combining previously distinct components of risk management and opening the path to a more comprehensive approach.

Future Recommendations

The limitations of the research should be tackled in the future studies using longitudinal designs to investigate causality and such changes. We can also extend the geographical coverage to obtain more localized picture of the problems. Further, the use of qualitative data collection approaches, for instance, Through case studies, or interviewing experts, would help enhance an understanding of context factors likely to affect governance or risk management. Finally, future research must extend knowledge about technological developments and their incorporation into management and risk reduction activities focusing on preserving organizational resilience against emerging catastrophic threats.

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