In the vast ocean of data that guides environmental policy, missing values can be like uncharted reefs, leading to skewed assessments and unfair penalties. This is particularly true for the Environmental Performance Index (EPI), a widely used tool that evaluates countries’ environmental sustainability. But fear not, for a team of researchers, led by Muhammed Haziq Muhammed Nor from the Department of Mathematical Sciences at Universiti Kebangsaan Malaysia, has set sail to navigate this maze of missing data, and their findings could have significant implications for the maritime sector.
The EPI assesses countries based on various indicators, including climate, environmental health, and ecosystem health. However, when data is missing, it can lead to biased outcomes. For instance, landlocked countries might be unfairly penalized for not having maritime activities, even if they access the sea through other means. “While it was assumed that landlocked countries could access the sea through various means, limited information was available,” Nor explained. To tackle this issue, Nor and his team compared three multiple imputation methods—MICEForest, k-Nearest Neighbors (k-NN), and MissForest—on EPI data with missing values ranging from 1% to over 50%.
So, what does this mean for the maritime sector? Well, the EPI isn’t just about environmental bragging rights. It can influence policy decisions, investment flows, and even maritime trade routes. Accurate EPI data can help identify countries that are genuinely lagging in environmental performance, allowing for targeted interventions and support. Conversely, it can also highlight maritime nations that are leading the way in sustainability, opening up opportunities for collaboration and investment.
The study, published in Environmental Research Communications, found that all three imputation methods produced data that closely matched the original, with minimal impact on central tendencies. However, MissForest and k-NN were found to be more stable and consistent than MICEForest across all error metrics when parameters were adjusted. This means that these methods could potentially provide a more accurate picture of a country’s environmental performance, leading to fairer assessments and more effective policy decisions.
For the maritime sector, this could open up new opportunities. For example, shipping companies could use this data to make more informed decisions about where to invest in green technologies or which routes to prioritize for sustainability initiatives. Port authorities could use it to identify areas for improvement and attract eco-conscious shipping lines. And for landlocked countries, it could provide a more accurate reflection of their environmental performance, potentially opening up new avenues for maritime cooperation.
But the journey doesn’t stop here. Nor and his team suggest that future research could explore deep learning techniques for handling missing data in environmental datasets like the EPI. As the maritime sector continues to grapple with the challenges of sustainability, such advancements could prove invaluable. So, let’s keep our eyes on the horizon, for the future of maritime sustainability is looking brighter than ever.