Spatiotemporal Evolution of the Environmental Adaptability Efficiency of the Agricultural System in China

Abstract

Since its emergence, the development of agriculture has always been closely related to changes in the natural environment. The productivity and development of agriculture largely depend on natural conditions and agriculture and has an important impact on the environment. The development of modern conventional agriculture has also led to a series of ecological, economic, and social problems that threaten human development and sustenance. China has historically been heavily reliant on agriculture and provides food and clothing for approximately 22% of the world’s population while only accounting for 9% of the world’s cultivated land and 6% of freshwater resources. Since the 21st century, the agricultural development of China has faced increasing resource and environmental constraints due to rapid industrialization and urbanization. Based on the perspective of efficiency evolution, data envelopment analysis (DEA) and spatial autocorrelation analysis (SAA) were used to test the environment adaptability efficiency within China’s agricultural systems across 30 provinces, autonomous regions, and municipalities, and explore its temporal and spatial evolution patterns and characteristics. Our study thus possesses both theoretical and practical significance. Furthermore, this study would enable the development of methods to assess China’s agricultural systems, in addition to providing a theoretical basis and guidelines for the creation of sustainable agriculture development strategies both in China and in other countries and regions. The following are the main conclusions of this study: (1) from 2000 to 2018, the overall environmental adaptability efficiency within China’s agricultural systems exhibited a gradual upward trend, achieving a transition from medium-level efficiency towards high-level efficiency, and the environmental adaptability of agricultural systems continued to increase. However, a certain gap remained between the level achieved and the DEA’s level of effectiveness, and therefore additional efforts are required to close this gap. (2) The environmental adaptability efficiency within China’s agricultural system showed a significant positive correlation in spatial distribution. Particularly, clear spatial aggregation characteristics were observed at the provincial level, which was also characterized by strong features of spatial dependence and spatial heterogeneity. Moreover, the degree of spatial aggregation increased gradually over time. High-value areas were mainly located along the southeast coastal area, whereas low-value areas were primarily located in the inland areas of the northwest. Therefore, environmental adaptability efficiency generally followed a northwest-southeast spatial distribution.

A systematic literature review of data envelopment analysis implementation in agriculture under the prism of sustainability

“…N leaching). Additionally, Tang et al ( 2022 ) included farm-specific undesirable variables such as soil erosion rate and grey water footprint in their DEA model, a characteristic that was absent from the other surveys. It should be noted that Grassauer et al ( 2021 ) and Rybaczewska-Błazejowska and Gierulski ( 2018 ) included LCA results as inputs in their DEA models in order to minimize the environmental effects of agricultural productivity.…”

“…8 shows, at the DEA stage half of the papers are contributing only to the economic aspect, 35% concerns both economic and environmental aspect, while only in 11% of the examined papers are representing all sustainability dimensions. For instance, Tang et al ( 2022 ) have incorporated land cost, HDI, annual precipitation and amount of water resources covering all three aspects of sustainability. Sánchez-Zamora & Gallardo-Cobos ( 2020 ) have embodied 22 indicators covering economic, environmental, social, institutional and spatial development characteristics.…”

Effects of local policies and public goods on tourism in rural areas: Exploring spatial dependence patterns

“…Tourism clusters in rural areas benefit from agglomeration when they are bound together with marketing channels and business transactions (Polo Peña et al, 2015;Tang et al, 2022;. This cluster approach for devel-oping tourism in rural areas accelerates business activities for rural inhabitants.…”

Data-Driven Evaluation and Optimization of Agricultural Environmental Efficiency with Carbon Emission Constraints

“…As research progresses, scholars have reached a realization that some undesired outputs inevitably occur in the agricultural production process, and ignoring undesired outputs may produce misleading results in assessing efficiency [43,44]. Considering that a large quantity of greenhouse gas is emitted in agricultural production, some scholars have started to include greenhouse gases as an undesired output in the evaluation index system, among which carbon emissions are mostly included as undesired outputs [45,46].…”

“…Considering the current carbon emission limits being pursued in many countries globally, agricultural carbon emissions, as an important overall contributor, should rightly be included in the studies of AEE [58,59]. Some scholars have addressed the measurement of carbon emissions in studies of environmental efficiency [45,46], but to our knowledge, the current literature has not been able to comprehensively include all elements of carbon emissions in the agricultural production process. In general, it is necessary to include carbon emissions as an undesired output in AEE evaluations, and the calculation of total carbon emissions requires further improvement.…”