Abstract— China has grown into the world's largest trading and foreign capital inflows country since 2010s and committed to reducing carbon emissions by 40%-45% by 2020. As the environmental governance in China has been raised to a new height, the focus of this paper will be placed on China's foreign trade and investment from the perspective of external economic variables, and empirical investigation will be conducted into how it has impacted on carbon emissions, which is believed as exerting a spatial auto-correlation effect among the provinces, with the assistance of the spatial econometric model under panel data on 30 provinces in 2001-2016. As demonstrated by the results, carbon emissions per capita in China bears a clustering effect significantly according to the Moran's I statistics. Furthemore, the estimates of spatial Durbin model with spatial fixed effects indicate that the level of economic growth and export value tend to cause carbon emissions to increase within a specific region, but mitigate it in the adjacent regions. However, the level of inward foreign investment leads to an opposite result, indicating that local governments ought to make the most of the spatial spillover effects in economic growth from adjacent regions, and development of complementary industries with high efficiency & low carbon emission. Meanwhile, local governments are supposed to be proactive in guiding the inward foreign investment into high & new technology-intensive industries, making the market accessible to foreign investment and putting in place the "Negative List System" in the field of clean energy.
Index Terms— Carbon emissions, China's external economy, spatial econometric model.
Zhiguang Song is with the Graduate School of Global Environmental Studies of Sophia University, Japan (e-mail: email@example.com).
Cite: Z. G. Song, " Spatial Effect of Carbon Emissions: A Perspective of China's External Economy by Spatial Econometric Model," International Journal of Environmental Science and Development vol. 11, no. 6, pp. 305-310, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).