Waste management, energy efficiency and R&D spending as drivers of carbon mitigation in Lithuania: a wavelet-based perspective

Purpose This paper aims to address the volatility of the trajectory of Lithuania’s carbon footprint and the limitations of current policy tools that fail to capture evolving complexity of emission drivers. The authors emphasize how static approaches overlook the time-varying influence of energy efficiency, renewable energy, R&D spending, waste management and economic growth. By highlighting this gap, the study aims to investigate dynamic, time-frequency relationships between Lithuania’s carbon footprint and its primary mitigation drivers. The objective is to reveal temporal “windows of opportunity” that support synchronized, adaptive and evidence-based carbon reduction strategies aligned with carbon neutrality goals for the long-term national policy agenda. Design/methodology/approach This study investigates the dynamic, time-frequency relationships between Lithuania’s carbon footprint and its primary mitigation drivers using advanced wavelet-based methods: wavelet transform coherence (WTC), partial wavelet coherence (PWC) and multiple wavelet coherence (MWC) from 1990 to 2022. Findings The results show that energy efficiency, renewable energy consumption and research and development spending significantly reduce emissions over time, while economic growth increases them. Waste management has a variable but supportive role. Findings reveal temporal “windows of opportunity” for impactful policy action. Originality/value The study offers original value as it applies a comprehensive suite of wavelet-based methods – WTC, PWC and MWC – to examine Lithuania’s carbon footprint dynamics, a novel approach in post-transition EU economies. By capturing time-frequency variation in the influence of energy efficiency, renewable energy, R&D spending, waste management and economic growth, the analysis exposes evolving causal structures that conventional static models overlook.