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| Author | Time | Sample | Method | Conclusions |
|
| Lai Zuoqing and Zhang Zhonghai (2008) | 2006 | 21 cities in Guangdong Province | DEA | The overall efficiency of Guangdong is high, and the relative efficiency of the nine cities is the best. The main reason for the low efficiency is that the forestry human resources are in the waste stage |
| Li Chunhua et al. (2011) | 2006 | 31 regions in China | DEA | Tianjin, Shanxi, Guangdong, and Guizhou have the best three efficiency values of forestry input-output. The forestry land area and government input are not fully utilized, resulting in low forestry efficiency |
| Li Wei et al. (2012) | 2008–2010 | 31 regions in China | DEA | The basic reason for the low output efficiency of some large forestry provinces is the idle labor resources and the low utilization rate of forest resources and capital |
| Tian Shuying and Xu Wenli (2012) | 1993–2010 | China | DEA | The average value of forestry input-output comprehensive efficiency in China is high, and forestry labor input and forestry primary industry output value are important factors affecting efficiency |
| Tian Shuying, Zhang Chen, and Xu Wenli (2013) | 1998–2013 | Anhui Province | DEA | The average value of forestry input-output comprehensive efficiency in Anhui Province is high, and forestry labor input and forestry primary industry output value are important factors affecting efficiency |
| Mi Feng, Liu Zhidan, Li Zhuowei, and Ji Yingxun (2013) | 1999–2011 | Gansu Province | DEA | The average value of forestry input-output comprehensive efficiency in Gansu Province is high, and forestry labor force factor is important |
| Yu Mingxia, Zhang Ziqiang, and Gao Lan (2014) | 1992–2012 | Guangdong Province | DEA Tobit | The efficiency in Guangdong Province is not high, and the population density in mountainous areas, rural per capita income, and the incidence of forest diseases and insect pests are significant factors |
| Xiong Xi, Cai GuiGui, Zhang Xuewen, and Zhang Qi (2014) | 2013 | Hunan Province | Cluster analysis | There are obvious regional differences in forestry input-output efficiency among cities and prefectures in Hunan, mainly due to the mismatch between input and output |
| Zhang Ying, Yang Guihong, and Li Zhuowei (2016) | 1993–2013 | Beijing | DEA | The average value of forestry input-output comprehensive efficiency in Beijing is high, and the complete amount of forestry fixed assets investment is an important factor affecting the efficiency of Lin Chao, Xie Zhizhong, and Cai Wenying (2016) |
| Lin Chao, Xie Zhizhong, and Cai Wenying (2016) | 2004–2013 | Fujian Province | DEA Malmquist | The efficiency value of Fujian Province shows an upward trend, and forestry labor input and technological progress are important factors |
| Wei Jingnan and Zhang Lizhong (2016) | 2000–2013 | Guangxi Province | DEA | In most years, Guangxi Province is in a state of inefficiency, and capital investment is an important factor |
| Liao Bing and Jin Zhinong (2014) | 2000–2011 | Jiangxi Province | DEA Tobit | The forestry output efficiency of Jiangxi Province has great growth potential, and the investment in forestry infrastructure and the salary of employees are important factors |
| Li Jingxuan, Chen Bingpu, and Yang Lujia (2017) | 1998–2014 | Gansu Province | DEA | The overall efficiency of Gansu Province is not ideal |
| Xiang Hongling, Chen Zhaojiu, Liao Wenmei, Ai Juan, and Zhang Mengling (2021) | 2007–2019 | 11 provinces (cities) in the Yangtze River economic belt. | DEA Malmquist | The labor transfer in the eastern region of the Yangtze River economic belt was conducive to the promotion of forestry TFP |
| Liu Dong, Wang Xin, and Lu Yuan (2021) | 2006–2018 | China | DEA Malmquist/SEM | China’s forestry total factor productivity was generally stable |
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