Wei Wang

The water footprint (WF) of crop production indicates the water consumption for crop growth in a specific area over a certain time, enabling comprehensive water use efficiency assessments to be achieved for different types of water. Improved spatial and temporal resolutions in quantification enable the water footprint (WF) in crop production to be a comprehensive indicator of water consumption in agricultural water management. However, in time, although daily and monthly blue (irrigation water) and green (rainfall) water resources are unevenly distributed in monsoon climate areas, the crop WF are generally recorded by years. In space, there is a lack of quantitative research on the effects of different spatial levels on the variation in crop WFs. Meanwhile, effect of developments in water-saving irrigation techniques on large-scale crop WF accounting is unclear yet.We conducted a series of case studies for China in order to address above three issues. In the first selected case for maize and wheat production in the Baojixia Irrigation District (BID) of Shaanxi province in the west China, the WF of crop production was analysed based on a regional distributed hydrological model and the associated meteorological driving factors on daily, monthly, and yearly scales were identified (Gao et al., 2021). The latter two case studies focused on wheat across the whole mainland China based on gridded crop WF simulations over the period 2000-2014. The WFs of wheat production at five different spatial levels, including crop field, county, river sub-basin, provincial, and large river basin were mapped followed by an analysis of meteorological and human management factors (Mao et al., 2021). The differences in terms of magnitudes, composition, and benchmarks of wheat WF under furrow, sprinkler and micro irrigation methods as well as rain-fed conditions were further distinguished and identified (Wang et al., 2019). Results revealed non-negligible effects of temporal and spatial scales on crop WFs. The possibility and importance to account for developments of water-saving techniques in regional crop WF estimations are shown as well. ReferencesGao, J., Xie, P., Zhuo, L., Shang, K., Ji, X., Wu, P. (2021) Water footprints of irrigated crop production and meteorological driving factors at multiple temporal scales. Agricultural Water Management, 255: 107014.Mao, Y., Liu, Y., Zhuo, L., Wang, W., Li, M., Feng, B. (2021) Quantitative evaluation of spatial scale effects on regional water footprint in crop production. Resources, Conservation & Recycling, 173: 105709.Wang, W., Zhuo, L., Li, M., Liu, Y., Wu, P. (2019) The effect of development in water-saving irrigation techniques on spatial-temporal variations in crop water footprint and benchmarking. Journal of Hydrology, 577: 123916.

The industrial sector is a major contributor to resource consumption and environmental pollution in China. The energy-intensive industrial development and energy structure are dominated by coal, which has produced an enormous amount of industrial pollutants in China, and put great pressure on the ecological environment. Hence, improving the performance of industrial green development (PIGD) has become an urgent task of utmost importance. This study applies a global non-radial directional distance function to estimate the PIGD for Jiangxi Province during 2003–2015, and provides targeted policy suggestions. The empirical results show a rising trend in the PIGD in Jiangxi Province. At the city level, Nanchang and Fuzhou performed considerably better than other cities in regards to their PIGD. However, the poor environmental performance caused by the excessive discharge of industrial pollutants has also hindered its PIGD. Most cities in Jiangxi Province failed to efficiently use resources, especially energy and labor, in industrial production. The results of the influencing factor analysis show that the performance of industrial green development in Jiangxi could be improved through increasing per capita GDP, decreasing the share of coal consumption in the total industrial energy consumption, and decreasing the share of industrial GDP in the total GDP. Furthermore, a more efficient use of environmental management investment funds and timely transfer of the surplus industrial labor are needed.

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.

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.

Using a sequential slack-based measure (SSBM) model, this paper analyzes the spatiotemporal disparities of urban land use economic efficiency (ULUEE) under environmental constraints, and its influencing factors in 270 cities across China from 2003–2012. The main results are as follows: (1) The average ULUEE for Chinese cities is only 0.411, and out of the 270 cities, only six cities are always efficient in urban land use in the study period. Most cities have a lot of room to improve the economic output of secondary and tertiary industries, as well as environmental protection work; (2) The eastern region of China enjoys the highest ULUEE, followed by the western and central regions. Super-scale cities show the best performance of all four city scales, followed by large-scale, small-scale and medium-scale cities. Cities with relatively developed economies and less pollutant discharge always have better ULUEE; (3) The results of slack variables analysis show that most cities have problems such as the labor surplus, over-development, excessive pollutant discharge, economic output shortage, and unreasonable use of funds is the most serious one; (4) The regression results of the influencing factors show that improvements of the per capita GDP and land use intensity are helpful to raise ULUEE. The urbanization rate and the proportion of foreign enterprises’ output account for the total output in the secondary and tertiary industries only have the same effect in some regions and city scales. The land management policy and land leasing policy have negative impact on the ULUEE in all the three regions and four city scales; (5) Some targeted policy goals are proposed, including the reduction of surplus labor, and pay more attention to environmental protection. Most importantly, effective implementation of land management policies from the central government, and stopping blind leasing of land to make up the local government’s financial deficit would be very helpful to improve the ULUEE of Chinese cities.