Evaluating the Spatiotemporal Characteristics of Agricultural Eco-Efficiency Alongside China’s Carbon Neutrality Targets

Abstract

Agriculture has the dual effect of contributing to both carbon emissions and sequestration, and thus plays a critical role in mitigating global climate change and achieving carbon neutrality. Agricultural eco-efficiency (AEE) is an important measurement through which we can assess the efforts toward reduced emissions and increased sequestration. The purpose of this study was to understand the relationship between China’s target of carbon neutrality and AEE through an evaluative model, so as to improve AEE and ultimately achieve sustainable agricultural development. The Super-SBM model scientifically measures the AEE based on provincial panel data collected between 2000 and 2020. We selected kernel density function and spatial distribution to explore the spatial and temporal evolutionary trends, and used a Tobit model to identify the drivers of AEE. The research shows that (1) China’s agricultural system functions as a net carbon sink, with all provinces’ agricultural carbon sequestration levels recorded as higher than their carbon emissions from 2000 to 2020. (2) Despite sequestration levels, the level of AEE in China is not high enough, and the average efficiency level from 2000 to 2020 is 0.7726, showing an overall trend where AEE decreased at first and then increased. (3) The AEE of each province is clearly polarized; there are obvious core–periphery characteristics and spatial distribution of clustered contiguous areas. Central provinces generally have lower efficiency, eastern and northeastern provinces have higher efficiency, and northeastern provinces always remain in the high-efficiency group. (4) Influencing factors show that urbanization, upgrading of industrial structure, financial support for agriculture, and mechanization have a significant positive impact on AEE. These findings have important implications for the promotion of the low-carbon green development of Chinese agriculture.

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.…”

Temporal-Spatial Dynamic Characteristics and the Determinants of Agricultural Eco-efficiency in Fujian Province, China

“…The emission factors for carbon in fertilisers, pesticides, and agricultural films, amongst others, are obtained from the "Guidelines for the Preparation of China's Provincial Greenhouse Gas Inventories." These emission factors are informed by the research results of the IPCC and the Oak Ridge National Laboratory in the United States [10].…”

“…The level of financial support for agriculture has the largest negative impact on Zhangzhou, followed by Nanping and Sanming, and gradually weakens, the coefficient of Ningde shows a trend of decreasing and then increasing, and the regression coefficients of Putian and Fuzhou experience a shift from positive to negative. The reason for this is that reasonable financial expenditure on agriculture can lead to scientific and technological innovation, enhance the training of talents, promote the transformation of agricultural production methods, and help improve regional AEE [10]. However, when the government's financial support for agriculture involves the ecological environment, due to the public good nature of the ecological environment, externalities tend to occur, resulting in market failures and the inability to allocate resources reasonably, leading to a decline in AEE [50].…”

“…Current research on AEE focuses on three main areas:(1) Construction of AEE evaluation index system, which usually includes labor, land, capital, fertilizer, pesticide, agricultural film, draft animal, machinery, etc. into the input indicators [7][8][9], classification of output indicators into desired and non-desired outputs, with gross agricultural output included in desired outputs, agricultural surface pollution and carbon emissions [10]. (2) In terms of evaluation methods, scholars mainly use data envelopment analysis (DEA) and its derived models [11,12] and non-expected SBM models [13], stochastic frontier analysis (SFA) [14], life cycle assessment (LCA), and other evaluation methods.…”

Can Agricultural Socialized Services Promote the Reduction in Chemical Fertilizer? Analysis Based on the Moderating Effect of Farm Size

“…For a long time, chemical fertilizer input has made great contributions to the increase in China's grain production, but there are still problems related to the high application amount of chemical fertilizer and low utilization efficiency. [3][4][5]. In 2019, the amount of fertilizer used per unit area of China was 2.87 times the world average (FAOSTAT), and the fertilizer utilization efficiency of the three major food crops was 39.2% [6], which is still a small gap from the 50%~60% in developed countries in Europe and America.…”