It is common to see urban land expansion worldwide, and its characteristics, mechanisms, and effects are widely known. As socio-economic transition and the change of land use policies may reverse the trend of urban expansion, in-depth research on the process of urban land use change is required. Through a process perspective, this paper reveals the change paths, development stages, and spatial patterns of urban residential land use with data from 323 cities in China from 2009 to 2016.

North Korea experienced a catastrophic famine in the mid-1990s that resulted in millions of deaths. This study aims to build an agent-based model to understand the risk of land degradation and famine in North Korea and explore potential solutions to mitigate this risk. The model concept reflects the general information of collective farms in North Korea, which was set in 1960, and the abstract of the social–ecological system of North Korean agriculture. The model comprises the agent, environment, and external factors.

Agent-based models (ABMs) are particularly suited for simulating the behaviour of agricultural agents in response to land use (LU) policy. However, there is no evidence of their widespread use by policymakers. Here, we carry out a review of LU ABMs to understand how farmers’ decision-making has been modelled. We found that LU ABMs mainly rely on pre-defined behavioural rules at the individual farmers’ level. They prioritise explanatory over predictive purposes, thus limiting the use of ABM for policy assessment.

Despite the significant and explicit focus on the United Nations Sustainable Development Goals (SDGs), much of the world’s land rights remain unrecorded and outside formal government systems. Blame is often placed on land administration processes that are considered slow, expensive, and expertise-dependent. Fit-For-Purpose Land Administration (FFPLA) has been suggested as an alternative, time and cost-effective approach.

Land is the key asset in the agricultural sector and hence land policy is one of the key elements that determine whether SDGs are achieved in developing counties or not. In developing countries, land titling programs have been seen as a strategy for addressing SDGs. Even though the government of Ethiopia launched the rural land registration and certification program (LRCP) to secure the land rights of rural households in 1998, currently, there are limited empirical studies to examine the contribution of LRCP in addressing sustainable development goals (SDGs).

With over 14 million hectares allocated, Vietnam’s forest and forestland allocation has been one of the largest natural resource decentralization programs in the developing world over the last three decades. Given this remarkable achievement, critics are concerned about the low rates of household tree planting investment and question the roles and effects of land institutions on investment.

Many studies have investigated the effects of large-scale land acquisition (LSLA) on livelihood, while the effects of LSLA by different actors on investment decisions and levels of investment have largely gone without academic scrutiny. Consequently, information concerning the implications of LSLA by actors on investment is scarce in the literature pertaining to policy.

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.