Spatial econometric analysis of environmental total factor productivity of ranimal husbandry and its influencing factors in China during 2001-2017

Pollution discharge from animal husbandry deteriorates the environment and is of global concern. In this study, data envelopment analysis (DEA) was used to indirectly estimate the pollution discharge of livestock and poultry breeding in terms of sustainable development in China. Land, biogas, and grassland were included as input indexes, and the inverted values of indexes of organic matter, nitrogen, and phosphorus produced by pigs, dairy cattle, beef cattle, poultry, and sheep were included as output indexes. Based on the DEA model with variable returns to scale, the pollution intensity index of livestock and poultry breeding was estimated using the principle of output maximization. This study focused on livestock and poultry farming pollution emissions, and the slack-based measure directional distance and metafrontier efficiency functions were used to measure the environmental total factor productivity (ETFP) of animal husbandry in each province and six major animal husbandry production regions of China during 2001-2017. Additionally, a spatial econometric model was employed to analyze the factors affecting animal husbandry ETFP. The results show that the mean value of animal husbandry ETFP was higher than that of conventional total factor productivity. The driver of increased animal husbandry ETFP was technological progress. Overall, China's animal husbandry was developing sustainably, and there was little scope for group technology to catch up. According to an empirical analysis of influencing factors, farmers' improved per capita income level and environmental governance helped to increase animal husbandry ETFP. Furthermore, various measures to improve animal husbandry ETFP in China according to local conditions are needed. Finally, animal husbandry should continue to develop sustainably, using environmental regulations that continuously exert the Porter Effect. (C) 2020 Elsevier B.V. All rights reserved.