Spatial planning systems and institutions have a significant role in managing non-agricultural land growth in Europe and the assessment of how their implementation impacts on agricultural land consumption is of great significance for policy and institutional improvement. Reducing the area of agricultural land taken for urban development, or eliminating such conversion, is an international policy priority aiming to maintain the amount and quality of land resources currently available for food production and sustainable development.

As a proxy for human activity, per capita urban land has great significance for urban planning. We still lack a comprehensive understanding of per capita urban land from the perspective of urban–rural gradients. Thus, based on the concentric buffering method and the dynamic-time-warp clustering method, this research analyzes the urban–rural gradient of the per capita urban land of 345 cities in China in 2000, 2010, and 2016. We find that the per capita urban land in China grew from 110.2 m2/person in 2000 to 118.9 m2/person, increasing by 7.9%.

Green development is necessary for building high-quality modern economic systems. Due to limited land resources, ensuring the ecological benefits will not be damaged while developing the economy is necessary, as is improving urban land green use efficiency (ULGUE). Lanzhou-Xining Urban Agglomeration is a crucial urban agglomeration in the upper reaches of the Yellow River Basin. The improvement of ULGUE can promote the integrated management of the region’s land and achieve harmonious development with the social economy.

Improving urban land use efficiency is a feasible way to realize sustainable development and alleviate urban land pressure on the city. The main purpose of this article is to measure the urban land use efficiency of the Yangtze River Economic Belt, and explore its evolutionary trends and influencing factors, so as to provide references for policy formulation to promote efficient land use and sustainable development.

In a digitalized era and with the rapid growth of computational skills and advancements, artificial intelligence and Machine Learning uses in various applications are gaining a rising interest from scholars and practitioners. As a fast-growing field of Artificial Intelligence, Machine Artificial Intelligence deals with smart designs, data mining and management for complex problem-solving based on experimental data on urban applications (land use and cover, configurations of the built environment and architectural design, etc.), but with few explorations and relevant studies.

In the context of tax sharing reform and land reform during the 1990s, local governments in China relied heavily on land finance. Local governments have fierce competition in attracting investment, omitting the development of green economy. Based on the data of industrial land sales and carbon dioxide emissions, this study constructed the panel data of 196 cities in China from 2007 to 2017 and analyzed the spatial and temporal evolution characteristics of urban industrial land price distortion and carbon emission intensity.

With continuous rises in GDP, land cover in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) has undergone a drastic change over the period 1990–2018. In this study, land use in the GBA was divided into six types: farmland, forestland, grassland, wetland, construction land, and unused land. We analyzed changes in spatiotemporal patterns according to region and type by using statistical analysis, spatial clustering, and hotspot analysis, focusing on the spatial characteristics of areas where land-use types changed with high frequency.

Baseflow is an essential component of runoff, which is the dominant water resource for the dry season. To better manage water resources, it is vital to investigate the links between the multiple influencing factors and the baseflow for better prediction in light of global changes. Previous studies have seldom separated these influencing factors in the analysis, making it difficult to determine their effect on the baseflow.

Global environmental changes are multifactorial and affected by multiple forms of land use. For this reason, and also in view of the current world climate scenario, they have become highly relevant and are subject to analysis and discussions on the best uses of land. The research presented here offers a systematic analysis on the priorities related to the multiple uses of land and their implications in urban planning. An exploratory and descriptive analysis is used with a qualitative approach based in a systematic literature review.

This paper explores the specific “authoritarian” type of adaptive governance of urban regeneration using the example of Guangzhou city as the frontier of China’s reforms. As opposed to the “democratic” type of adaptive governance with its bottom-up policy initiations, community autonomy, polycentric power, participation in decision making, and self-organized policy actors, adaptive governance in Guangzhou is based on top-down decision making and implementation of public authorities’ solutions with the high role of political considerations.

With the intensification of the contradiction between living space and population growth, it is necessary to improve the effectiveness of urban residential land allocation. This study systematically reviews 169 papers following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to collect and collate the contributing factors that affecting the supply of and demand for urban residential land for different countries, and a statistical analysis of long-term series data is conducted to further verify the rationality of the contributing factors.

The quantitative urban system structure in historical periods and the long time-scale urban land area grid dataset with spatial attributes are important for land use and land cover change (LUCC) research. In this study, we aimed to measure the area of county level and above cities in mainland China in the 1930s, also known as the traditional Chinese era (TCE), using a geographic information system (GIS) model and 1:50,000 military topographic maps.