Tackling multicollinearity in greenhouse gas emission drivers: Ridge and Liu-Based mixed model approaches
DOI:
https://doi.org/10.35208/ert.1649373Keywords:
Environmental statistics, greenhouse gas emissions, linear mixed models, Liu prediction, multicollinearity, Ridge predictionAbstract
Greenhouse gases (GHGs) such as water vapor (H₂O), carbon dioxide (CO₂), nitrous oxide (N₂O), methane (CH₄), ozone (O₃), and other harmful gases are the major drivers of global warming, as they absorb solar radiation and trap heat in the atmosphere. Identifying the factors that influence GHG emissions is therefore crucial for tackling climate change, safeguarding public health, and ensuring environmental sustainability. This study examines the role of six industrial sectors—agriculture, forestry and fishing (AFF); manufacture of food, beverages and tobacco (FBT); manufacture of paper and paper products (PPP); manufacture of chemicals and chemical products (CCP); water supply, sewerage, waste management and remediation (WSR); and transportation and storage (TS)—in shaping GHG emissions across 17 Eurostat countries between 2010 and 2021. To address the issue of multicollinearity among sectoral variables, we apply ridge and Liu estimators within a linear mixed model framework. The findings advance statistical methodologies for environmental data analysis and provide insights into the primary industrial drivers of GHG emissions. By highlighting the most influential industrial sectors and demonstrating the advantages of ridge and Liu estimators in handling multicollinearity, this study offers a pathway for more accurate climate modeling and evidence-based policymaking. These approaches can support the design of targeted mitigation strategies and guide future climate analytics toward more reliable and interpretable outcomes.
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