Spatiotemporal characteristics and influencing factors of agricultural low-carbon economic efficiency in china

Abstract

Developing low-carbon agriculture can effectively avoid the waste of natural resources, thus contributing to the long-term sustainability of agriculture. This study uses the Super-SBM model to measure agricultural low-carbon economic efficiency (ALEE) in China from 2000 to 2018, then analyzes the spatial-temporal evolution characteristics. Simultaneously, the influencing factors of ALEE are investigated using spatial econometric model. The results show that: (1) In terms of temporal evolution, the ALEE in most provinces is declined over time, with only a few provinces improving. The ALEE in China and the three regions all show an obvious “L” trend of decline first and then stability. (2) From the perspective of spatial differentiation, provinces in eastern region have higher ALEE, while those in central and western regions have lower ALEE. Hainan’s ALEE has an absolute advantage, while Shanxi is the worst. (3) China’s ALEE shows obvious spatial agglomeration characteristics of H-H and L-L agglomeration, which are further enhanced over time. The number of L-L agglomeration provinces gradually increases, indicating that China’s ALEE has not been improved significantly. (4) Economic growth level and Agricultural scientific and Technological progress have effectively improved the ALEE. However, Capital deepening, Government fiscal expenditure, Agricultural planting structure, and Agricultural disaster all have negative impacts. Rural electricity consumption also has a negative impact, but the impact is not significant. To accelerate the development of low-carbon agriculture, all regions must not only pursue a differentiated low-carbon agriculture development path, but also accelerate agricultural transformation, strengthen research and development, and popularize low-carbon agricultural technologies, reduce the input of traditional agricultural means of production, optimize the agricultural industrial structure, and adjust agricultural subsidy policies.

Re-measurement and influencing factors of agricultural eco-efficiency under the ‘dual carbon’ target in China

“…The output indicators are the expected outputs, including the total output values of agriculture, forestry, animal husbandry and fishery and the entire sector [ 21 , 22 ]. Secondly, the choice of input variables is similar to the first category, but the selection of output indicators distinguishes between desired and undesired outputs, with desired outputs involving variables, such as gross agricultural output, gross agricultural, forestry and fishery products, value of ecosystem services and food production [ 23 , 24 ], and undesired outputs involving variables, for instance, surface source pollution and carbon emissions [ [25] , [26] , [27] ]. Given the significant difference between AEE results considering and not considering non-desired outputs, the former is more realistic [ 28 ]; the second idea of indicator selection is receiving increasing attention from scholars.…”

“…Given the characteristics of the range of values of explanatory variables in this paper, instead of adopting a permutation measure for the test, the substitution variable method was applied for the robustness test. Drawing from the work of [ 23 ] on the use of food crop sown area/total crop sown area to characterise the AIS, this paper used this indicator to replace the original AIS indicator, with other variables remaining unchanged and re-tests being conducted at the national and regional levels. Table 5 presents the Tobit regression results.…”

Spatial Distribution of Green Total Factor Productivity in Chinese Agriculture and Analysis of Its Influencing Factors

“…The spatial differentiation of geographical entities can be influenced by elements of the economy, society, and natural environment [30,31]. To investigate the elements influencing the changes to green TFP in agriculture, taking into account the accessibility of data, relevant variables from natural conditions, agricultural production conditions, agricultural technology level, agricultural economic development level, and agricultural support policies were selected as driving factors using relevant studies [32]. Table 4 shows the calculation process of each factor, mainly using the quartile classification method to categorize the independent variables.…”

A county-scale assessment of ecosystem health in the Three Gorges Reservoir area based on catastrophe theory

“…Fertilizer application amount and pesticide application amount together measure the degree of agricultural pollution and reflect the quality of agricultural environment in different periods (Meng et al, 2018). Excessive use of agricultural materials such as chemical fertilizers and pesticides has led to large-scale agricultural nonpoint source pollution (Ma et al, 2022). Therefore, when establishing environmental subsystem indicators, the use of chemical fertilizers and pesticides should be taken into account as indicators affecting environmental pollution.…”