Provincial cultivated land use efficiency in China: Empirical analysis based on the SBM-DEA model with carbon emissions considered

Bing Kuang, Xinhai Lu, Min Zhou and Danling Chen

Technological Forecasting and Social Change, 2020, vol. 151, issue C

Abstract: Rapid urbanization and industrialization has worsened the situation of the scarce cultivated land resources of China. It's therefore of great importance for sustainable development based on the systematic evaluation on cultivated land use efficiency (CLUE). This study took carbon emissions resulting from cultivated land use into the measurement framework of CLUE, and a slack-based measure (SBM) model with undesirable outputs, boxplot, kernel density estimation and Tobit regression model are adopted for the analysis of 31 provinces in China from 2000 to 2017. The results showed that there was an increasing trend in CLUE in China from 0.5236 in 2000 to 0.8501 in 2017, with the growth rate of 38.40%. Most of provinces in China have much lower levels of CLUE with significantly spatial disparities. In particular, Hainan, Chongqing, Sichuan and Guizhou are always most efficient with the highest value of 1. At the regional level, the average value of CLUE in the northeastern region is the highest, followed by the western, eastern and central regions, and the CLUE in the eastern region is more unstable than the other three regions. The results of Tobit regression show that natural conditions, cultivated land resource endowments, agricultural production conditions, regional economic development and regional science and technology development are important factors resulting in the disparity of China's CLUE.

Keywords: Cultivated land use efficiency; Slack-based measure model; Undesirable outputs; Carbon emissions; China (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (74)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162518311545
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:151:y:2020:i:c:s0040162518311545

DOI: 10.1016/j.techfore.2019.119874

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().