Resource-based cities in China face the dual pressure of environmental pollution and unemployment. Therefore, it is necessary to measure the effect of environmental regulation on employment. In this study, we first analyzed the theoretical mechanism of employment effects of environmental regulation. Second, we constructed a nonlinear panel threshold regression model with industrial structure rationalization and optimization as the threshold variables and used data from 115 resource-based prefecture-level cities to empirically examine the impact of environmental regulation on employment. The results demonstrate that 1) There is a significant threshold effect between environmental regulation and employment in resource-based cities, with the rationalization and optimization of the industrial structure gradually crossing the threshold from a low threshold to a high threshold, and the impact of environmental regulation on employment has gradually changed from an inhibitory effect to a promotion effect; 2) This conclusion still holds after the robustness test and the division of life cycles of different types of resource-based cities; 3) The coal resource cities as a representative of this kind of resource-based cities with serious environmental pollution, strengthening environmental regulation, have an obvious role in promoting employment. This study enriches the research content of environmental regulation on employment and provides useful references for rational improvement of unemployment in China.

The Transfer Payment Policy of National Key Ecological Functional Areas (TPEFAP), a well-known ecological compensation (eco-compensation) scheme in China, has been proposed by the government to alleviate ecological poverty and protect the environment. In literature, the effectiveness of the TPEFAP on environmental conservation has been widely examined, while few pay attention to the effect of the TPEFAP on poverty alleviation, especially with the consideration of its spatial spillovers as well. In this paper, we utilize panel data covering the key ecological functional areas of China during the period 2011–2018 to evaluate the impact of the TPEFAP on poverty alleviation and also its spatial spillovers by employing the synthetic control method (SCM) and the dynamic spatial Durbin model, respectively. Specifically, we apply the entropy weight method (EWM) to calculate the multidimensional poverty index (MPI) and measure pro-poor effect in terms of MPI change. The results show that: (1) TPEFAP has stable positive effects on MPI in Hubei, Yunnan, Jilin, Gansu, and Ningxia, while the impact on Qinghai fluctuates. (2) MPI presents a significant spatial correlation. Furthermore, both the direct and indirect effects of TPEFAP on MPI are significant and stable positive, for both short- or long-term. (3) For potential channels, rural non-farm employment, rural labor mobility, and agricultural productivity are the key pathways through which the TPEFAP can alleviate poverty both in local and adjacent provinces. However, it is difficult to find significant positive spatial spillovers for the TPEFAP if only the natural resources scale is considered. This study indicates that the government should pay attention to the policy expectations of ecological poverty alleviation and, in future eco-compensation, must further increase the coverage of subsidies and diversify the forms of subsidies.

Foreign direct investment (FDI) technology spillovers play an increasingly important role in a host country’s development. Evaluating the positive effect of FDI inflows on green innovation is essential for correct city design. Based on the panel data of 262 cities in China from 2004 to 2018, we first analyzed the impact of FDI technology spillovers on green innovation in Chinese cities and then tested the threshold effect in four absorptive capacity factors: environmental regulation, economic growth, human capital, and industry size. Finally, we compared the time and space of two types of cities crossing the threshold from the perspective of innovative and non-innovative cities. The results show that FDI can significantly promote green innovation in Chinese cities and the promoting effect of FDI on green innovation has nonlinear characteristics, namely, such effects only make sense when absorptive capacity is above the threshold points. Additionally, among the four absorptive capacity factors, the development degrees of innovative cities are ahead of non-innovative cities; in particular, there is a significant difference between them in terms of economic growth. Local governments should develop reasonable policy combination tools according to the absorptive capacity characteristics of different cities to effectively promote the technology spillover effect of FDI and achieve coordinated ecological and economic development.

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