Agricultural Carbon Emission Efficiency and Its Driving Forces: NDDF Measurement, Malmquist Decomposition, and Convergence Evidence from China’s Counties

This paper evaluates agricultural carbon emission efficiency (ACEE) at China's county level during 2021–2024. Quantitatively, mean ACEE rises from 0.617 in 2021 to 0.678 in 2024, while dispersion narrows (σ of ln(ACEE): 0.124→0.110, −11.3%). Malmquist results indicate persistent productivity gains (ML>1 in all adjacent periods), with technical change weakening (TC: 1.025←1.012) and efficiency catchup remaining stable (EC≈.04). A non-radial directional distance function (NDDF) with undesirable outputs is applied to jointly consider agricultural output, input use, and carbon emissions. Dynamic changes are assessed using a Malmquist productivity index decomposed into efficiency change and technical change, while σ-convergence and β-convergence tests are conducted to evaluate catch-up patterns. A two-way fixedeffects panel model is further used to identify key drivers of ACEE. Results suggest that mean ACEE increased steadily and dispersion declined, indicating both performance improvement and narrowing gaps across counties. Technological progress was the primary contributor in the early stage, whereas efficiency catch-up gained importance later. Farm scale, mechanization, and digital adoption are positively associated with ACEE, while fertilizer intensity and rapid urbanization exert negative impacts. Policy implications emphasize precision input management and differentiated regional technology diffusion.