An Analysis of the Spatiotemporal Characteristics and Diversity of Grain Production Resource Utilization Efficiency under the Constraint of Carbon Emissions: Evidence from Major Grain-Producing Areas in China

Abstract

As the most important driving force for ensuring the effective supply of grain in the country, the production stability of the major grain-producing areas directly concerns the national security of China. In this paper, considering the “water–soil–energy–carbon” correlation, water, soil and energy resource factors, and carbon emission constraints were included in an index system, and the global common frontier boundary three-stage super-efficient EBM–GML model was used to measure the grain production resource utilization efficiency of the major grain-producing areas in China from 2000 to 2019. This paper also analyzed the static and dynamic spatiotemporal characteristics and the restrictions of utilization efficiency. The results showed that, under the measurement of the traditional data envelopment analysis model, the grain production resource utilization efficiency in the major producing areas is relatively high, but there is still room to improve by more than 20%, and grain production still has enormous growth potential. After excluding external environmental and random factors, it was found that the utilization efficiency of grain production resources in the major producing areas decreased, and the efficiency and ranking of provinces changed significantly. External factors inhibit pure technical efficiency and expand the scale efficiency. The utilization efficiency of Northeast China was much higher than that of the Huang-Huai-Hai region and the middle and upper reaches of the Yangtze River region, and its grain production resource allocation management had obvious advantages. The total factor productivity index of food production resources showed an upward trend as a whole, and its change was affected by both technological efficiency and technological progress, of which technological progress had the greater impact. Therefore, reducing the differences in the external environment of different regions while making adjustments in accordance with their own potential is an effective way to further improve the utilization efficiency of food production resources.

The challenges of sustainable energy transition: A focus on renewable energy

“…However, standard DEA models have a few issues. The epsilon-based measure DEA [62] , the slack-based measure DEA [63] , and the global Malmquist-Luenberger model [64] are some examples of enhanced DEA models that have emerged from these types of investigations. In order to achieve the goal of green development, factors such as economic openness [65] , industrial structure, energy consumption structure, urbanization rate, government spending, technological innovation, and environmental regulation need to be investigated.…”

Assessing Land Resource Carrying Capacity in China’s Main Grain-Producing Areas: Spatial–Temporal Evolution, Coupling Coordination, and Obstacle Factors

“…The division of China's MGPAs originated from the reform of the grain circulation system in 2001 [65]. Based on the general characteristics of grain production and consumption, as well as historical traditions and resource disparities, 31 provinces in China (excluding Hong Kong, Macau, and Taiwan) are categorized into three major grain functional areas: the MGPAs (13 provinces), the balanced production and marketing areas (11 provinces), and the major grain-marketing areas (7 provinces).…”

The effect of urbanization on agricultural eco-efficiency and mediation analysis

“…With the increase in rural residents' disposable income by 100 CNY (China yuan), AEE increased by 9.5% (Liu et al, 2020b). However, Wang and Huang (2022) examined the relationship between rural residents' income and AEE in China's major grain-producing areas. They argued that rural residents' income negatively affected AEE because less of the increased income was invested in improving agricultural production conditions.…”

“…(1) Dependent variable: agricultural eco-efficiency (AEE). AEE refers to a process that seeks to maximize agricultural economic output while minimizing resource consumption and negative environmental impact (Coluccia et al, 2020;Wang and Huang, 2022;Zhou et al, 2022). Therefore, this study constructed an AEE evaluation indicator system, as shown in Table 1.…”