*4.4. Risk Response Strategies and Their Effects*

In the preceding section, the two most common risk response strategies for the markets were defined as demand compression and emergency supply. Similar tests were conducted in four scenarios: no risk response, emergency supply only, demand compression only and the two strategies together. As shown in Figure 6, both strategies were effective for responding to the overall risks, and the system's resilience was the highest when both strategies were applied collaboratively. However, situations may arise in which decisionmakers have to choose between the two alternatives due to restricted resources and time.

**Figure 6.** Effects of the risk response strategies: (**a**) the behavior of the coupled markets' satisfaction rate for different risk response strategies; (**b**) the behavior of the capacity of natural gas reserves for different risk response strategies.

To properly investigate the long-term effects of different strategies, we extended the experimental duration from 180 days to 360 days. It can be observed that in the previous short-term experiments, the emergency supply of natural gas quickly brought the coupled market back to normal in the face of minor disturbances, and that even in a more severe risk scenario (e.g., when import shortages occurred), its response efficiency was also higher. At the end of day 180, the satisfaction rate of the coupled natural gas–electricity market using the emergency supply strategy was 71.28%, while the demand compression strategy achieved a rate of 69.49%. However, the improvements brought about by the emergency supply of natural gas stagnated with time, and demand compression became the superior strategy after day 297. This fact derives from the restrictions in the capacity of the natural gas reserves. While supplementation by additional natural gas is beneficial for mitigating the demand–supply gap induced by risks, the reserve capacity of a specific country is limited. Without boosting that capacity, this strategy will collapse if a crisis persists for an extended period of time.

#### **5. Conclusions**

By considering the complexity of multiple risks, the interactions among risks and market interactions, this study provided a comprehensive and transparent overview so that decision-makers could understand the evolving patterns of the risks influencing the coupled natural gas–electricity market. It first describes a list of the prominent risk factors and dual interactions based on a literature review and by tracking news about real-world accidents. Subsequently, a system dynamics model was constructed for the risk assessment. Four causal feedback loops were formulated that captured the dynamism and complexity embedded in the evolution of the coupled markets. Using China as an example, all variables were determined using China's Energy Statistical Yearbook, China's Statistical Yearbook, and other open and reliable data sources. After the construction of the model, three experiments were conducted, investigating the impact of each individual risk factor on the coupled market, the dynamic behaviors of the markets considering the dual interactions and a comparison of the two risk response strategies. The main findings are as follows.


The following are policy recommendations based on the findings presented above: An isolated and static perspective of risk assessment is inevitably inaccurate; instead, monitoring the process and controlling the overall market are required to avert crises. Using

the developed approach, decision-makers can identify when various disruptions may occur and which risk factors account for their occurrence and keep an eye on impending severe risks. For countries like China who have started embracing a new era of clean energy, determining the degree of long-term interactions between multiple energy markets is vital to guarantee energy security. In addition, among the risk response strategies, while the emergency supply strategy soon recovered the markets, the compression of demand had a longer enduring impact. Hence, decision-makers should strike a balance between the short-term and long-term effects of strategies, rather than adopting a myopic view.

The contribution of this study manifests in three aspects. First, it establishes an integrated framework for multiple stakeholders from different sectors to have a more systematic look at the underlying risks, with the objective of enhancing the overall performance of the coupled market. Second, the proposed model quantitatively captures both the stochastic nature of risks and the nonlinearity of interactions, offering a cost-effective and dynamic instrument that supports the whole risk assessment process through explicit experiments. Third, the visualization results in transparent graphics can help decision-makers to easily examine the evolutionary impact of risks and compare the consequences of various policies. Some limitations also exist that inspire future research. For instance, while the functional interactions are under investigation, geographical interactions may also contribute to the propagation of risks. It is possible to better characterize the complexity of relationships by using hybrid models that incorporate both geographical and functional information. Moreover, due to the complexity embedded, this study focuses primarily on how risks may result in a supply–demand imbalance and how various strategies will mitigate the gaps. Since resources in practice are often limited, when developing risk response strategies, multiple factors regarding the financial constraints and the carbon emissions can also be considered.

**Author Contributions:** Conceptualization and methodology, L.W.; validation and data curation, Y.X. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (no. 22YJC630137), the Shanghai Yang Fan Program (no. 22YF1401600), and the Fundamental Research Funds for the Central Universities (no. 2232022E-04).

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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