Measuring green total factor productivity of China's agricultural sector: A three-stage SBM-DEA model with non-point source pollution and CO2 emissions

China is the most populous country in the world, and agriculture has played a large role in the country’s remarkable economic achievements. However, the high levels of pollution and emissions from agriculture pose a threat to the nation’s food and ecological security. Using a global weighted Russell distance function model, this paper attempts to provide strategies for the green development of China’s agriculture by investigating the historical green total factor productivity. The results show that (1) during the sample period, China’s agricultural green total factor productivity (AGTFP) showed a u-shaped trend, and the turning point was 2010. Technological progress in terms of agricultural output, energy use and pollutant treatment was the most powerful factor of growth after 2010, while the technological retrogression in terms of capital use was the main obstacle to growth. (2) Eastern China has the highest AGTFP, while western China has the highest AGTFP growth rate. This result indicates that eastern China is a leader in green agricultural development, while green agricultural development in western China is the most promising. The “mid-China collapse” occurred in the central China agricultural area. The collapse of the central region was mainly caused by a decline in pure technical efficiency; more specifically, the pure technical efficiency of fertilizer severely restricted the sustainable agricultural development of this area. (3) The provinces that have great spatial differences in terms of agricultural development and the driving factors of AGTFP can develop suitable green agricultural development strategies based on the results presented in this study.

What drives the fluctuations of “green” productivity in China’s agricultural sector? A weighted Russell directional distance approach

Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries

This study aims to explore the dynamic association among crop production, livestock production, power consumption in agriculture, forest area, and carbon dioxide (CO2) emissions Based on the annual data of China, spanning the period 1990 to 2016, the study applied the auto-regressive distributed lag (ARDL) bounds testing approach In addition, the fully modified ordinary least squares (FMOLS) canonical cointegration regression (CCR) and the Granger causality tests are employed to check the robustness of the ARDL estimations The ARDL-bounds testing approach indicated that all variables share a long-run connection The long- and short-run ARDL estimations confirmed that crop production, as well as livestock production, has a significant positive effect on CO2 emissions in both cases However, power consumption in agriculture and forest area has a negative effect on it, indicating that both variables reduce CO2 emissions in the long and short run These results stood robust under various regression estimators and confirmed the findings of the ARDL method Additionally, the results of the causality approach specified that a unidirectional causality is running from crop production, power consumption in agriculture, and forest area to CO2 emissions The causality between livestock production and CO2 emissions is bidirectional Therefore, the directions of this connection also validate the outcomes under various techniques used for robustness These findings suggest that the government must reconsider its policies related to agricultural and livestock production and adopt environment-friendly practices in the agriculture sector that may reduce the carbon footprints in the long run

Dynamic relationship among agriculture-energy-forestry and carbon dioxide (CO 2 ) emissions: empirical evidence from China

Measuring China's agricultural green total factor productivity and its drivers during 1998-2019.

This paper investigates total-factor energy efficiency and analyses the trends of the efficiency changes in China’s agricultural production across 30 provinces and three regions from 2001 to 2011, based on data envelopment analysis (DEA) approach. The potential amount of energy savings and five potential factors for energy efficiency improvement are also empirically studied by Tobit regression model. The findings show that (1) total-factor energy efficiency in China’s agricultural sector is increasing over years but performs heterogeneously across regions; (2) agriculture intensive regions and energy abundant provinces tend to be relatively energy inefficient in agricultural production; and (3) economic structure, agricultural production structure, technological progress and income effect are major potentials for improving energy efficiency, whereas energy price is not a significant factor. This phenomenon results from the divergence of economic development, endowment effects as well as the scale of agricultural production. Policy implications drawn from this research are to upgrade industrial structure and promote agricultural transformation to enhance farmers’ income as well as to establish a land market with entitling land property rights to farmers. This conclusion can assist to form more scientific rural energy policy decision-making in China and also can be extended to other developing economies for sustainable agriculture.

https://www.mdpi.com/1996-1073/11/4/853/pdf

Anlu Zhang

Papers

Total-Factor Energy Efficiency in China’s Agricultural Sector: Trends, Disparities and Potentials