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With the increasingly obvious restriction of the ecological environment on economic development, environmental regulations are widely used to achieve “green production,” that is, to improve green total factor productivity (GTFP). First, through the econometric model, it can be concluded that command-based environmental regulations could improve GTFP, while market-based environmental regulations have no significant impact on GTFP. Unlike traditional econometric models, machine learning has no specific data requirements and research assumptions. We use Lasso regression to verify the above results by obtaining the optimal tuning parameter. Furthermore, considering that the leap of China’s economy is inseparable from foreign direct investment (FDI), we use FDI as a threshold variable. The threshold model results showe that when the intensity of FDI in China ranges between 1.2492 and 1.588, both types of environmental regulations can significantly promote GTFP. These conclusions passed the robustness test. Given the differences in economy and resource endowment among different regions in China, a regional heterogeneity test is conducted. The results show that the current environmental regulations in eastern and central China have no significant impact on GTFP. However, when the intensity of FDI in central China is greater than 3.6868, environmental regulations have a significant promoting effect on GTFP. In western China, when FDI intensity ranges between 1.3950 and 1.5880, market-based environmental regulations can significantly promote GTFP. Further, the path test of the mediation effect model reveals that command-based environmental regulations reduce GTFP by reducing FDI. The above conclusions provide empirical data for the intensity of FDI in different regions of China to improve GTFP.

frontiers in environmental science

Exposing the Effects of Environmental Regulations on China’s Green Total Factor Productivity: Results From Econometrics Analysis and Machine Learning Methods