In the context of China’s trial fallow policy; the heavy metal pollution of farmland is addressed via field surveys in Hunan Province, where the fallow policy has been implemented, and in Jiangxi Province, where it has not been implemented. We measured and analyzed willingness to accept (WTA) using the contingent valuation method (CVM). The conclusions of this study are as follows: (1) Farmer awareness of heavy metal pollution and pollution sources is higher in Jiangxi Province than in Hunan Province; (2) Ignoring the impact of other factors, the WTA of farmers is 902 (yuan /mu) in Jiangxi Province and 902.26 (yuan /mu) in Hunan Province. Considering the influence of the basic characteristics of the respondents using the parameter estimation method, the WTA of farmers is 839.34 (yuan/mu) in Jiangxi Province and 934.39 (yuan/mu) in Hunan Province. There is little difference in WTA between the two provinces, but both estimates are higher than the national compensation standards; (3) The factors that affect the WTA of farmers in Jiangxi Province are gender, education level, average annual income and per capita arable land. The factors that affect the WTA of farmers in Hunan Province are age, education level, family size, average annual income, per capita arable land area and farmer occupation; (4) At present, the means and methods of compensation for the implementation of the fallow policy are recognized by most farmers. The paper concludes with some policy suggestions based on above findings.

Industry is a major contributor to carbon emissions in China, and industrial land is an important input to industrial production. Therefore, a detailed analysis of the carbon emission performance of industrial land use is necessary for making reasonable carbon reduction policies that promote the sustainable use of industrial land. This paper aims to analyze the dynamic changes in the total-factor carbon emission performance of industrial land use (TCPIL) in China by applying a global directional distance function (DDF) and non-radial Luenberger productivity index. The empirical results show that the eastern region enjoys better TCPIL than the central and western regions, but the regional gaps in TCPIL are narrowing. The growth in NLCPILs (non-radial Luenberger carbon emission performance of industrial land use) in the eastern and central regions is mainly driven by technological progress, whereas efficiency improvements contribute more to the growth of NLCPIL in the western region. The provinces in the eastern region have the most innovative and environmentally-friendly production technologies. The results of the analysis of the influencing factors show implications for improving the NLCPIL, including more investment in industrial research and development (R&D), the implementation of carbon emission reduction policies, reduction in the use of fossil energy, especially coal, in the process of industrial production, actively learning about foreign advanced technology, properly solving the problem of surplus labor in industry and the expansion of industrial development.

More attention needs to be paid to efficiency in the use of forested land. This article is devoted to the study of forested land use efficiency (FLUE) and its spatiotemporal differences in China during the period from 1999 to 2010. The global generalized directional distance function (GGDDF) and global Malmquist–Luenberger (GML) index models are used to measure and analyze forested land use efficiency. The empirical results showed that forested land use efficiency continued to increase during the study period. The FLUE of Shanghai was always highest, whereas Tibet, Inner Mongolia, and Qinghai suffered the most inefficiency in forested land use. There were obvious spatial differences in forested land use efficiency among the 31 provinces. Urbanization, economic development context, and population density were the main factors influencing spatial differences in forested land use efficiency. The growth in the non-radial Malmquist forested land performance index (NMPFI) in the east was driven mainly by technological change, whereas the growth in the central region was mostly derived from progress in efficiency change. For the western region, the change in the productivity of forested land was the result of the interactive effect between technological change and effect change, and only in the western region did an absolute β-convergence exist.