The Yangtze River Economic Belt (YREB) is a major national strategic development area in China, and the development of the YREB will greatly promote the development of the entirety China, so research on its agricultural production efficiency is also of great significance. This paper is committed to studying the agricultural production efficiency of 11 provinces in the YREB and adopts a combination of the Data Envelopment Analysis (DEA) model and the Malmquist index to make a dynamic and static analysis on the YREB’s agricultural production efficiency from 2010 to 2019. Then, a three-stage DEA Malmquist model that eliminates the factors of random interference and management inefficiency is compared to a model without elimination. The results show that the adjusted technological efficiency changes, technological progress, and total factor productivity increased by −0.1%, 0.24%, and 0.22%, respectively. When comparing these values to the pre-adjustment values, the results indicate that the effect of environmental variables cannot be ignored when studying the agricultural production efficiency of the YREB. At the same time, the differences in the agricultural production efficiency in the YREB are reasonably explained, and feasible suggestions are put forward.

Carbon reduction is an important aspect of achieving high-quality environmental development; environmental regulation and industrial agglomeration are important ways to affect carbon emissions. Therefore, studying the relationship between industrial agglomeration, environmental regulation, and carbon emissions has important theoretical and practical significance. Firstly, this article adopts the method of location entropy to measure the indicators of industrial agglomeration. Secondly, it proposes an environmental regulation indicator system based on the experience of previous scholars and measures the environmental regulation indicators using the entropy method. Next, eight types of energy consumption are used to measure carbon emissions based on the carbon emission coefficient method provided by the IPCC. Finally, based on the Moran index, the spatial correlation of carbon emission levels in various regions of the Yangtze River Economic Belt (YREB) is tested. A spatial econometric model was introduced to explore the relationship between industrial agglomeration, environmental regulation, and carbon emissions at a deeper level, and the following conclusions were drawn: (1) The regression coefficient of the spatial term of industrial agglomeration on carbon emissions is 0.848, which is significantly positive at the 10% level, indicating that under the influence of spatial effects, industrial agglomeration has a significant promoting effect on carbon emissions. (2) The regression coefficient of the spatial term of environmental regulation on carbon emissions is −0.011, which is significantly negative at the 10% level, indicating that environmental regulation has an inhibitory effect on carbon emissions under the influence of spatial effects. Based on the above conclusions, useful suggestions have been provided for optimizing industrial structure, improving environmental regulation levels, and alleviating carbon emission issues.

Traditionally, in the pursuit of economic development, ecological health was sacrificed, but this is no longer tenable considering the vast environmental damage that is difficult to control and expensive to repair. This is especially true for China’s Jianghuai River Basin (JRB). As a result, this paper uses the slacks-based measure (SBM) directional distance function and the tobit model on panel data from various cities and counties in the Jiangsu section of the HRB to empirically analyze regional green agricultural production efficiency and influencing factors from 2005 to 2019. The results illustrate that agricultural and environmental efficiency fluctuate upward in provincial areas. Still, a downward trend is observed in both redundancy and insufficiency rates of undesirable and desirable output. While this indicates improving regional agricultural and environmental efficiency, there is no readily detectable positive effect of technological progress and technical efficiency on green agricultural production efficiency improvements. Following a full analysis, policy implications are presented and discussed.