Spatial double dividend from China’s main grain-producing areas policy: total factor productivity and the net carbon effect

Abstract

Abstract Because of the reductions in China’s cultivated land area and the significant impact on the agricultural market, the main grain-producing areas (MGPA) policy has become vitally important for ensuring China’s food security. However, guaranteed food security and sustainability require ecological security, which raises the question of whether food production should come at the expense of the environment. This study used 1998–2020 panel data from 30 Chinese provinces and a spatial difference-in-differences model (SDID) to explore the MGPA policy effects and mechanism paths on agricultural total factor productivity (ATFP) and the net carbon effect (NCS). It was found that economically, the MGPA policy promoted local ATFP improvements and had positive spillover effects on surrounding provinces through factor diffusion, and environmentally, the MGPA policy had a positive effect in the MGPA policy implementation areas but inhibited the NCS in surrounding provinces. Further explorations of the environmental performances revealed that the MGPA policy promoted growth in agricultural carbon sinks and agricultural carbon emissions, with the environmental performances being primarily reflected in an increase in carbon sinks and a decline in the carbon emissions density. The mechanism test showed that the MGPA policy’s local environmental performance was achieved through economic performance. The ATFP was refined into technical innovation (TI), technology adoption (TA), and business scale (OS), with the mechanistic roles of these three refining paths being OS > TI > TA. A further mechanism test revealed that the MGPA policy effects on the ATFP were influenced by the various adjustment of production support, government support, and collaborative support. Based on the above analysis, this study gives policy recommendations to ensure food security and the realization of China’s dual carbon target.