Spatiotemporal Differentiation of Carbon Emission Efficiency and Influencing Factors in the Five Major Maize Producing Areas of China
Understanding the carbon emission efficiency (CEE) of maize production and its determinants is critical to supporting China’s dual-carbon goals and advancing sustainable agriculture. This study employs a super-efficiency slack-based measure model (SBM) to evaluate the CEE of five major maize-produci...
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MDPI AG
2025-07-01
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| author | Zhiyuan Zhang Huiyan Qin |
| author_facet | Zhiyuan Zhang Huiyan Qin |
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| description | Understanding the carbon emission efficiency (CEE) of maize production and its determinants is critical to supporting China’s dual-carbon goals and advancing sustainable agriculture. This study employs a super-efficiency slack-based measure model (SBM) to evaluate the CEE of five major maize-producing regions in China from 2001 to 2022. Kernel density estimation and the Dagum Gini coefficient are used to analyze spatiotemporal disparities, while a geographically and temporally weighted regression (GTWR) model explores the underlying drivers. Results indicate that the national average maize CEE was 0.86, exhibiting a “W-shaped” fluctuation with turning points in 2009 and 2016. From 2001 to 2015, the Southwestern Mountainous Region led with an average efficiency of 0.76. Post-2015, the Northern Spring Maize Region emerged as the most efficient area, reaching 0.90. Efficiency levels have generally become more concentrated across regions, though the Southern Hilly and Northwest Irrigated Regions showed higher volatility. Inter-regional differences were the primary source of overall CEE disparity, with an average annual contribution of 46.66%, largely driven by the efficiency gap between the Northwest Irrigated Region and other areas. Spatial heterogeneity was evident in the impact of key factors. Agricultural mechanization, cropping structure, and environmental regulation exhibited region-specific effects. Rural economic development and agricultural fiscal support were positively associated with CEE, while urbanization had a negative correlation. These findings provide a theoretical foundation and policy reference for region-specific emission reduction strategies and the green transition of maize production in China. |
| format | Article |
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| language | English |
| publishDate | 2025-07-01 |
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| spelling | doaj-art-9f1c9ffa5afa4a979053eacaae9fe8692025-08-20T03:02:55ZengMDPI AGAgriculture2077-04722025-07-011515162110.3390/agriculture15151621Spatiotemporal Differentiation of Carbon Emission Efficiency and Influencing Factors in the Five Major Maize Producing Areas of ChinaZhiyuan Zhang0Huiyan Qin1College of Economics & Management, Northeast Forestry University, Harbin 150040, ChinaCollege of Economics & Management, Northeast Forestry University, Harbin 150040, ChinaUnderstanding the carbon emission efficiency (CEE) of maize production and its determinants is critical to supporting China’s dual-carbon goals and advancing sustainable agriculture. This study employs a super-efficiency slack-based measure model (SBM) to evaluate the CEE of five major maize-producing regions in China from 2001 to 2022. Kernel density estimation and the Dagum Gini coefficient are used to analyze spatiotemporal disparities, while a geographically and temporally weighted regression (GTWR) model explores the underlying drivers. Results indicate that the national average maize CEE was 0.86, exhibiting a “W-shaped” fluctuation with turning points in 2009 and 2016. From 2001 to 2015, the Southwestern Mountainous Region led with an average efficiency of 0.76. Post-2015, the Northern Spring Maize Region emerged as the most efficient area, reaching 0.90. Efficiency levels have generally become more concentrated across regions, though the Southern Hilly and Northwest Irrigated Regions showed higher volatility. Inter-regional differences were the primary source of overall CEE disparity, with an average annual contribution of 46.66%, largely driven by the efficiency gap between the Northwest Irrigated Region and other areas. Spatial heterogeneity was evident in the impact of key factors. Agricultural mechanization, cropping structure, and environmental regulation exhibited region-specific effects. Rural economic development and agricultural fiscal support were positively associated with CEE, while urbanization had a negative correlation. These findings provide a theoretical foundation and policy reference for region-specific emission reduction strategies and the green transition of maize production in China.https://www.mdpi.com/2077-0472/15/15/1621carbon emission efficiencyChina’s five major maize-producing regionssuper-efficiency SBM modelgeographically and temporally weighted regression |
| spellingShingle | Zhiyuan Zhang Huiyan Qin Spatiotemporal Differentiation of Carbon Emission Efficiency and Influencing Factors in the Five Major Maize Producing Areas of China Agriculture carbon emission efficiency China’s five major maize-producing regions super-efficiency SBM model geographically and temporally weighted regression |
| title | Spatiotemporal Differentiation of Carbon Emission Efficiency and Influencing Factors in the Five Major Maize Producing Areas of China |
| title_full | Spatiotemporal Differentiation of Carbon Emission Efficiency and Influencing Factors in the Five Major Maize Producing Areas of China |
| title_fullStr | Spatiotemporal Differentiation of Carbon Emission Efficiency and Influencing Factors in the Five Major Maize Producing Areas of China |
| title_full_unstemmed | Spatiotemporal Differentiation of Carbon Emission Efficiency and Influencing Factors in the Five Major Maize Producing Areas of China |
| title_short | Spatiotemporal Differentiation of Carbon Emission Efficiency and Influencing Factors in the Five Major Maize Producing Areas of China |
| title_sort | spatiotemporal differentiation of carbon emission efficiency and influencing factors in the five major maize producing areas of china |
| topic | carbon emission efficiency China’s five major maize-producing regions super-efficiency SBM model geographically and temporally weighted regression |
| url | https://www.mdpi.com/2077-0472/15/15/1621 |
| work_keys_str_mv | AT zhiyuanzhang spatiotemporaldifferentiationofcarbonemissionefficiencyandinfluencingfactorsinthefivemajormaizeproducingareasofchina AT huiyanqin spatiotemporaldifferentiationofcarbonemissionefficiencyandinfluencingfactorsinthefivemajormaizeproducingareasofchina |