As an important grain-producing area in China, research on the spatial correlation network of grain production in Northeast China is of great significance to ensure food security and realize the sustainable development of grain production. Based on the data of 40 cities in Northeast China from 1999 to 2019, we used the modified gravity model and social network analysis method to explore the structural characteristics of the spatial correlation network of grain production. Then, we divided the network into four blocks—net spillover block, main beneficial block, broker block, and bidirectional spillover block—and explored the interactive relationships and spillover effects between blocks. On this basis, corresponding policy recommendations were put forward. The results are as follows. (1) The spatial correlation network of grain production in Northeast China presents a complicated development trend, but the overall tightness of the network still needs to be improved. (2) The spatial correlation network of grain production is characterized by multi-center distribution, in which important nodes not only play the role of central actors but also act as intermediaries and bridges in the network. (3) There are obvious spatial correlations and spillover effects between blocks, and it is in the agglomeration stage of the agglomeration–diffusion effect.

The manufacturing industry is the pillar industry of China’s economy and a major carbon emitter, and its carbon emission reduction efforts directly determine whether the country’s carbon emission reduction target can be successfully met. In the context of the goals of the carbon peak and carbon neutrality policy, we examine the impact of manufacturing structure optimization on carbon emissions from 2003 to 2020 through a spatial econometric model, taking the old industrial centers in Northeast China as an example. We then apply a machine learning model to simulate manufacturing carbon emissions during the carbon peak stage and identify the optimal path for carbon emission reduction, which is important for promoting manufacturing carbon emission reduction in Northeast China. Since the goal of low-carbon economic development has gradually replaced the goal of maximizing economic efficiency in recent years, manufacturing structure optimization has come to focus on energy saving and emission reduction. Therefore, we define manufacturing structure optimization from the dual perspective of technology and energy consumption to broaden the existing research perspective. The results show the following: (1) The overall trend in manufacturing structure optimization in Northeast China is steadily improving, and the level of manufacturing structure optimization from the technology perspective is higher than that from the energy consumption perspective. (2) Manufacturing structure optimization and manufacturing carbon emissions in Northeast China both show a positive spatial correlation. Manufacturing structure optimization in Northeast China can effectively promote carbon emission reduction, and it also has a spatial spillover effect. (3) The carbon emission reduction effect of manufacturing structure optimization from the energy consumption perspective is better than that from the technology perspective, and the carbon emission reduction effect under the institutional innovation scenario is better than that under the baseline scenario and the technological innovation scenario. Focusing on manufacturing structure optimization from both technology and energy consumption perspectives, as well as continuously improving technological innovation and institutional innovation, can help to achieve manufacturing carbon emission reduction in Northeast China.