Analysis of regional agricultural carbon emission efficiency and influencing factors: Case study of Hubei Province in China

Abstract

In recent years, China’s industrial economy has grown rapidly and steadily. Concurrently, carbon emissions have gradually increased, among which agricultural production is an important source of greenhouse gas emissions. It is necessary to reduce agricultural carbon emissions by improving their efficiency to achieve the global goal of peak carbon dioxide emissions in 2030. From a dynamic and static point of view, this study puts agricultural carbon emissions into the evaluation index system of agricultural carbon emission efficiency and analyzes the agricultural carbon emission efficiency and its influencing factors in Hubei Province. First, the unexpected output Slacks-based measure (SBM) model in data envelopment analysis was used to evaluate the agricultural carbon emission efficiency of Hubei Province in 2018 and compared it with other provinces horizontally. Second, the Malmquist–Luenberger index was used to analyze the comprehensive efficiency of agricultural carbon emissions in Hubei Province from 2004 to 2018. The role of technological progress and technical efficiency change in the development of low-carbon agriculture in Hubei Province was analyzed. The results showed that agricultural production efficiency in Hubei Province improved from 2004 to 2018, and the overall level was slightly higher than the average level in China. However, agriculture has not eliminated the extensive development modes of high input, low efficiency, high emission, and high pollution. The efficiency of technological progress in agricultural resource utilization in Hubei Province was close to the optimal level. The improvement space was small. Hence, the low efficiency of agricultural technology is a key factor restricting the improvement of agricultural production efficiency. The results provide a reference for low-carbon agricultural policy formulation and expand the policy choice path. This has practical significance.

Research on the Level of Agricultural Green Development, Regional Disparities, and Dynamic Distribution Evolution in China from the Perspective of Sustainable Development

“…The following abbreviations are used in this manuscript: NCER (Northeast Comprehensive Economic Region); NCCER (Northern Coastal Comprehensive Economic Region; ECCER (Eastern Coastal Comprehensive Economic Region); SCER (Southern Coastal Economic Region); YRCER (Yellow River Basin Comprehensive Economic Region); YRBCE (Yangtze River Basin Comprehensive Economic Region); GSCER (Great Southwest Comprehensive Economic Region); GNCER (Great Northwest Comprehensive Economic Region). Province numbers: Liaoning (6), Jilin (7), Heilongjiang (8), Beijing (1), Tianjin (2), Hebei (3), Shandong (15), Shanghai (9), Jiangsu (10), Zhejiang (11), Fujian (13), Guangdong (19), Hainan (21), Shaanxi (27), Shanxi (4), Henan (16), Inner Mongolia (5), Hubei (17), Hunan (18), Jiangxi (14), Anhui (12), Yunnan (25), Guizhou (24), Sichuan (23), Chongqing (22), Guangxi (20), Gansu (28), Qinghai (29), Ningxia (30), Tibet (26), Xinjiang (31).…”

“…Currently, within the academic community, there are three main approaches to measuring and analyzing the level of agricultural green development: calculating green total factor productivity [8][9][10], estimating agricultural carbon emissions [11][12][13], and calculating the comprehensive indices of agricultural green development [14][15][16]. Existing research outcomes in this field are significant for exploring green development, offering valuable insights for the direction of this study.…”

Prediction model and demonstration of regional agricultural carbon emissions based on PCA-GS-KNN: a case study of Zhejiang province, China

“…The industrial structure and carbon emissions situation in Zhejiang province is analyzed and the impact of industrial structure on carbon emissions intensity is examined Zhu and Zhang (2021). The role of technological progress and technical efficiency change in the development of low carbon agriculture in Hubei Province is analyzed (Shan et al 2022). Based on the estimation of the match between agricultural carbon emissions and soil and water resources, the log-mean partition index (LMDI) model is used to study the relationship between soil and water resource development and agricultural carbon emissions (Zhao et al 2018).…”

The Effect of Heterogeneous Environmental Regulations on Carbon Emission Efficiency of the Grain Production Industry: Evidence from China’s Inter-Provincial Panel Data

“…China is the largest carbon emitter in the world, which strongly affects global climate change [1]. Apart from its increasing industrial carbon emissions, agricultural carbon emissions of China are also at a high level and have become an important source of greenhouse gas emissions [2,3]. Despite China's surging grain output in recent years, which has greatly contributed to national grain security and nutrition security [4].…”

“…It is also necessary to study the differential impact of heterogeneous environmental regulations on the carbon emission efficiency of the grain planting industry so as to provide a reference for the Chinese government to improve environmental regulation tools. (3) Furthermore, the relationship between environmental regulations and carbon emission efficiency of the grain planting industry may not be simply linear, so we used the threshold model and intermediary model proposed by Hansen [25] and Baron and Kenny [26] to deeply analyze the relationship between them and explore the mechanism of heterogeneous environmental regulations on carbon emission efficiency of grain planting industry.…”