Nowadays, China is the world’s second largest economy and largest carbon emitter. This paper calculates the carbon emission intensity and the carbon emissions per capita of land use in 30 provinces at the national level in China from 2006 to 2016. A spatial correlation model is used to explore its spatiotemporal features. The results show that (1) China’s land use carbon emissions continued to grow from 2006 to 2016. The spatial heterogeneity of carbon emission intensity of land use initially decreased and then increased during this period. The carbon emission of land use pattern reached a peak in 2015 and the land use carbon emission intensity was relatively lower in east China; (2) southern China accounts for a majority of the total Chinese carbon sink. Better economic structure, land use structure and industrial structure will lead to lower carbon emission intensity of land use; (3) carbon emissions per capita of land use in China are affected not only by land development intensity, urbanization level, and energy consumption structure, but also by the population policy. It is significant to formulate differentiated energy and land use policies according to local conditions. This study not only provides a scientific basis for formulating different carbon emission mitigation policies for the local governments in China, but also provides theoretical reference for other developing countries for sustainable development. It contributes to the better understanding of the land use patterns on carbon emissions in China.

Exploration of urban spatial connections and network structures of urban agglomeration in the Yangtze River Delta, as well as its influencing factors, is of great significance regarding optimization of the development pattern of the Yangtze River Delta urban agglomeration and promotion of regional high-quality development. Therefore, based on Baidu index data in 2015 and 2019, this paper first analyzes the spatiotemporal variation characteristics of information-flow connections in the Yangtze River Delta urban agglomeration. Then it uses social network analysis to explore the information-flow network structure in the Yangtze River Delta urban agglomeration, and finally explores the influencing factors of information-flow intensity in the Yangtze River Delta urban agglomeration. The main conclusions are as follows: (1) The total amount of information flow in the Yangtze River Delta urban agglomeration has had no obvious change, and the coverage of information flow in the central urban circle has expanded. (2) The network hierarchy presents a relatively stable “pyramid” distribution pattern, which tends to develop into a “spindle” pattern. (3) The overall network density of the Yangtze River Delta urban agglomeration is high and is increasing. The backbone network is a “triangle” structure. The central cities in the region are stable, and the subgroups are adjacent to each other geographically. (4) Gross Domestic Product, resident population of the region and the number of Internet broadband subscribers all have important effects on the total information flow, among which the number of Internet broadband subscribers has the greatest effect on the total information flow. In addition, urban functions and their positioning, urban events, history and culture, and other factors that are difficult to quantify also have a certain impact on the information-flow network among cities.

The major grain-producing areas will be the key areas of future China fallow. It is important to explore the influence of farmers’ value perceptions on their fallow willingness in these areas. We analyzed this impact of value perception by using an ordered PROBIT model and survey data from the major grain-producing areas of Hubei and Hunan, China. The conclusions of this study are as follows: (1) A considerable proportion of farmers are willing to participate in farmland fallow, while a considerable proportion of farmers are neutral; (2) farmers’ value perceptions of farmland fallow have a significant positive impact on their fallow willingness; (3) farmers’ ages and education levels have a positive impact on farmers’ willingness to directly participate in farmland fallow, while per capita farmland area has a negative impact; (4) the key factors for successful fallow are solving the problem of non-agricultural employment of farmers and appropriately formulating fallow mode, scale, and subsidy standards. This study proposes that the government can develop farmers’ good value perceptions of fallow through appropriate subsidies and adequate publicity to strengthen their fallow consciousness.

Studying the spatial-temporal distribution industrial sprawl in China is important to solve industrial sprawl problems and promote urban sustainable development. This paper constructed a multi-level spatial analysis of the Chinese industrial sprawl during 2010–2019 by mainly using urban scaling law, supplemented by GIS methods. Results showed that: (1) China had obvious industrial sprawl with a growth rate of 31.79%, reaching 2762.37 km² between 2010 and 2019. (2) There was a stronger industrial sprawl in large cities with a larger population according to urban scaling law, especially in the East. (3) The industrial sprawl was mainly concentrated in the cities in the Northeast, Beijing-Tianjin-Hebei region, Shandong Peninsula, Yangtze River Delta region, Pearl River Delta region, Middle Yangtze River region, Fujian Province, and some cities in the West. (4) The gravity center of industrial sprawl generally moved southwest and distributed in Hubei Province. This study provided references for improving the efficiency of industrial land use and promoting high-quality urban development.

In this study, an inexact stochastic fuzzy programming (ISFP) model is proposed for land-use allocation (LUA) and environmental/ecological planning at a watershed level, where uncertainties associated with land-use parameters, benefit functions, and environmental/ecological requirements are described as discrete intervals, probabilities and fuzzy sets. In this model, an interval stochastic fuzzy programming model is used to support quantitative optimization under uncertainty. Complexities in land-use planning systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The proposed method is applied to planning land use/ecological balance in Poyang Lake watershed, China. The objective of the ISFP is maximizing net benefit from the LUA system and the constraints including economic constraints, social constraints, land suitability constraints, environmental constraints, ecological constraints and technical constraints. Modeling results indicate that the desired system benefit will be between [15.17, 18.29] × 10¹² yuan under the minimum violating probabilities; the optimized areas of commercial land, industrial land, agricultural land, transportation land, residential land, water land, green land, landfill land and unused land will be optimized cultivated land, forest land, grass land, water land, urban land, unused land and landfill will be [228234, 237844] ha, [47228, 58451] ha, [20982, 23718] ha, [33897, 35280] ha, [15215, 15907] ha, [528, 879] ha and [1023, 1260] ha. These data can be used for generating decision alternatives under different scenarios and thus help decision makers identify desired policies under various system-reliability constraints of ecological requirement and environmental capacity. Tradeoffs between system benefits and constraint-violation risks can be tackled. They are helpful for supporting (a) decision of land-use allocation and government investment; (b) formulation of local policies regarding ecological protection, environment protection and economic development; (c) analysis of interactions among economic benefits, system reliability and ecological requirements.

Under the background of New-type Urbanization, with the continuous advancement of urbanization and the all-round development of cities, all kinds of demands are also rising. In the case of demand, it is difficult to quickly adjust from the land supply side and to guide the optimization of the structure and layout of land use is one of the methods to achieve this based on the current situation and shortage of urban land use structure and spatial arrangement. Because of the complexity, uncertainty and dynamics of the land use system, it is necessary to use an uncertain model to accurately describe and propose the approximate optimal solution, so this study analyzes the influencing mechanism of land use and optimize the land use structure under uncertainties by using a Bayesian network and fuzzy mathematical programming. Based on the results of the two stages of analysis, the cellular automata simulation is completed. The framework is applied to Chongzhou city in western China. The results indicated that the optimal land space for cultivated land is in the middle and the south based on the joint influence probability of arable land and urban construction land. The conversion probability of the area near the east is low, and the joint impact probability of construction land in all areas is generally similar except for the western protection area. After the optimization of the fuzzy planning, the optimal construction land scale is 69.42 km². Under the condition that the cultivated land’s red line is guaranteed, there is still 98.87 km² of space for the increase in cultivated land. It is found through simulation that the increase in construction land would occur in the central and western parts of Chongzhou, which may be caused by the urban siphon effect. According to Monte Carlo verification, when the conversion probability exceeds 50%, the cultivated land could be turned into urban construction land, with an accuracy of 91.99%. Therefore, this proposed framework is helpful to understand the process of land use and provides a reference for making scientific and reasonable territorial spatial planning and guiding land use practice under uncertainties.