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China is known for its large industrial sector and diversified energy mix, which could contribute to environmental pollution, as fossil fuels remain China's main source of energy. With the recent drive by the Chinese government to achieve low carbon emissions and further reduce greenhouse gases, this study adds to the existing literature by combining the quantile-on-quantile (QQ) regression and non-parametric techniques to examine the role of economic complexity, nonrenewables energy and renewable energy consumption on the ecological footprint in China over the period 1985Q1–2019Q4. Overall, results show that renewable energy, non-renewable energy use, economic growth and economic complexity affects ecological footprint positively. In addition, the nonparametric causality outcomes revealed that renewable energy, non-renewable energy use, economic growth and economic complexity can significantly predict variations in ecological footprint at different quantiles. We are of the opinion that policymakers in this region should work on the pro-growth mentality of China, which is majorly fossil fuel-driven. This requires an immediate replacement with more eco-friendly sources and energy-saving technologies for economic activities. Otherwise, fulfilling the SDG 13 goals in China will be challenging. For a sustainable renewable energy investment, China should shift to ancillary and spot markets, where the low energy storage and low marginal cost of renewable energy could facilitate higher reduction in electricity cost and encourage higher trading of electricity. [ABSTRACT FROM AUTHOR]

This paper examines the impact of tourist arrivals on environmental degradation, as well as highlights the implications of this interaction on environmental sustainability targets, while controlling for globalization, energy consumption and economic growth. The study adds to the existing literature by employing the quantile-on-quantile regression (QQR) to examine the nonlinear relationship between carbon emissions and the regressors over the period 1995-2018, using quarterly data. As a robustness check, the conventional quantile regression (QR) model is also applied to capture the nexus across quantiles (0.1-0.9). Furthermore, quantile causality is utilized to capture the causal interrelationships between CO2 and the regressors across quantiles (0.1-0.90). Overall, results show that globalization, tourist arrivals, economic growth and energy consumption increase environmental degradation at different quantiles. The quantile causality results also reveal evidence of causality in mean and variance from globalization, tourist arrivals, economic growth and energy consumption to CO2 emissions in majority of the quantiles. From a policy standpoint, it is recommended that policymakers in Thailand should come up with sound and effective environmental policies that would help the economy achieve sustainable environmental quality for both the immediate and future generations. [ABSTRACT FROM AUTHOR]