With growing global warming issues, the association between technological innovation and environmental pollution has created significant debate in recent years. This paper examines the long-run and causal impact of technological innovation, economic growth, and renewable energy on consumption-based carbon emissions in Japan. The study utilized quarterly data spanning between 1990 and 2015. The study utilized recent econometrics techniques such as Maki co-integration, ARDL bunds test, FMOLS, DOLS, and frequency domain causality techniques. To the author's understanding, no prior studies have been conducted in Japan using consumption-based carbon emissions as a proxy of environmental degradation. Thus, this empirical analysis contributes to the literature. The findings from the ARDL bounds and Maki co-integration tests revealed evidence of co-integration among the series. The results of FMOLS and DOLS reveal that both renewable energy and technological innovation improve the environmental quality, while economic growth harms the quality of the environment. The results of the frequency-domain causality technique reveal that technological innovation, renewable energy, and economic growth can significantly predict consumption-based carbon emissions in Japan. Based on these outcomes, we suggested that Japan's government should be careful when formulating policies that trigger growth, which will have a detrimental impact on the environmental quality. Our empirical outcome also revealed that any policy that encourages renewable energy should be encouraged since it enhances environmental quality.

This paper explores long-run and causal effects of financial development, real growth, urbanization, gross capital formation and energy consumption on CO2 emissions in Thailand by utilizing recent econometric techniques. The study employs ARDL technique to examine the long and short run interconnection between CO2 emissions and the regressors. Furthermore, we employ the FMOLS, DOLS and CCR as a robustness check to the ARDL long-run estimator. The study use time-series data spanning from 1971 to 2016. The study also utilizes the wavelet coherence technique to collect information on the association and causal interrelationship among these economic variables at different frequencies and timeframes in Thailand. The study objectives are structured to answer the following questions: (a) does the selected macroeconomic indicators impact CO2 emissions in Thailand? (b) if so, why? Findings reveal; (i) Negative and insignificant link between CO2 emissions and urbanization. (ii) GDP growth affects CO2 emissions positively. (iii) The interconnection between CO2 emissions and energy usage is positive. (iv) Gross capital formation impact CO2 emissions positively. (v) Positive interconnection exists between financial development and CO2 emissions in Thailand. Additionally, the wavelet coherence result provides a supportive evidence for the ARDL long run result. Based on these findings, policy directions were suggested.