Land & Climate Change

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Seeking land use development strategies is an effective policy tool to support economic development, especially in developing countries. Previous studies evidence the indispensable role of urban construction land use (UCLU) in regional economic development. However, neglecting the two-stage characteristic and mismatch of UCLU could misinterpret the strategy.