Modeling the dynamic impacts of maritime network blockage on global supply chains.

Recent phenomena such as pandemics, geopolitical tensions, and climate change-induced extreme weather events have caused transportation network interruptions, revealing vulnerabilities in the global supply chain. A salient example is the March 2021 Suez Canal blockage, which delayed 432 vessels carrying cargo valued at $92.7 billion, triggering widespread supply chain disruptions. Our ability to model the spatiotemporal ramifications of such incidents remains limited. To fill this gap, we develop an agent-based complex network model integrated with frequently updated maritime data. The Suez Canal blockage is taken as a case study. The results indicate that the effects of such blockages go beyond the directly affected countries and sectors. The Suez Canal blockage led to global losses of about $136.9 ($127.5-$147.3) billion, with India suffering 75% of these losses. Global losses show a nonlinear relationship with the duration of blockage and exhibit intricate trends post blockage. Our proposed model can be applied to diverse blockage scenarios, potentially acting as an early-alert system for the ensuing supply chain impacts. Furthermore, high-resolution daily data post blockage offer valuable insights that can help nations and industries enhance their resilience against similar future events.

Evaluating Losses from Water Scarcity and Benefits of Water Conservation Measures to Intercity Supply Chains in China.

The severe water scarcity in China poses significant economic risks to its agriculture, energy, and manufacturing sectors, which can have a cascading effect through the supply chains. Current research has assessed water scarcity losses for global countries and Chinese provinces by using the water scarcity risk (WSR) method. However, this method involves subjective functions and parameter settings, and it fails to capture the adaptive behaviors of economies to water scarcity, compromising the reliability of quantified water scarcity loss. There is a pressing need for a new method to assess losses related to water scarcity. Here, we develop an agent-based complex network model to estimate the inter-regional and intersectoral impacts of water scarcity on both cities and basins. Subsequently, we evaluate the supply chain-wide economic benefits of four different water conservation measures as stipulated by the 14th Five-Year Plan for the Construction of a Water-Saving Society. These measures include increasing the utilization rate of recycled water in water-scarce cities, reducing the national water consumption per industrial value-added, and implementing agricultural and residential water conservation measures. Results show that direct losses constitute only 9% of the total losses from water scarcity. Approximately 37% of the losses can be attributed to interregional impacts. Among the water-scarce cities, Qingdao, Lanzhou, Jinan, and Zhengzhou pose a significant threat to China's supply chains. Agricultural water conservation yields the highest amount of water savings and economic benefits, while residential water conservation provides the highest economic benefit per unit of water saved. The results provide insights into managing water scarcity, promoting cross-regional cooperation, and mitigating economic impacts.

Sliding Window-Based Machine Learning for Environmental Inspection Resource Allocation.

Environmental regulation is pivotal in mitigating environmental risks and promoting sustainable development, yet regulators frequently encounter resource constraints when inspecting enterprises. To address this limitation, we employed four sliding window-based machine learning techniques to enhance effective environmental inspections. Utilizing feature-engineered time-series data of characteristic and compliance records from 16,777 chemical enterprises in Jiangsu between 2010 and 2021, the four models were used to establish the predictive models that link enterprise characteristics to the likelihood of inspection failure. The results indicated that the models were comparable with the widely utilized deep learning sequence model, the Long Short-Term Memory model, achieving areas under the ROC (receiver operating characteristic) curves exceeding 0.83. Past violation significantly influences future violations, with a more recent violation history exerting stronger impacts. Besides, using predicted failure rates, we proposed seven resource allocation scenarios, considering different regulation intensities to target high-risk enterprises. Notably, the provincial-level risk-based method yielded a significant inspection failure rate increase (detecting violation), surpassing 8-fold the baseline. Overall, our study offers regulators an optimized inspection resource allocation method, thereby enhancing the regulatory effectiveness. Moreover, this study demonstrates the potential of window-based machine learning techniques in environmental regulation and highlights the importance of data-driven decision-making for promoting sustainable development.