Exploring Land Use Scenarios in Metropolitan Areas: Food Balance in a Local Agricultural System by Using a Multi-objective Optimization Model

Consequences of aggregating spatial units in an interactive multiple-goal linear programming (IMGLP) model are analysed for a schematized and an existing IMGLP model (GOAL) exploring land use options for the European Union. A discrimination was made between effects on objective functions for the system as a whole, and effects on related optimum land use allocation within the system. In GOAL, effects on land use allocation are more important than effects on the value of objective functions. Several rules or factors were identified that determine the effect of aggregation, among which the degree in curvilinearity in input-output relations and the method of aggregation are important ones. However, because of complicated interacting effects, the aggregation error is difficult to predict. Therefore, in land use studies using IMGLP it is important to first optimize the linear programming model at the non-aggregated level and then aggregate to the appropriate policy level. If aggregation is inevitable because LP models become too big, aggregation according to agro-ecological criteria, i.e., aggregation of units with similar output-input ratios and constraints, results in the smallest errors.

An assessment of a collaborative mapping approach for exploring land use patterns for several European metropolises

Combining system dynamic model and CLUE-S model to improve land use scenario analyses at regional scale: A case study of Sangong watershed in Xinjiang, China

Rapid expansion of urban built-up areas since the 1950s has led to the Milan region becoming one of the major metropolitan areas of Europe. This has been accompanied by significant structural changes to urban and peri-urban landscapes and fragmentation of formerly contiguous green corridors by the distribution of new urban forms such as housing and transport infrastructure. The need to address the loss of green space was first recognised by policy-makers at the end of the 1970s and in due course, this has led to new policies and laws. These policies included the introduction of the Milan metropolitan parks approach that, nowadays, is represented by numerous urban forests that have become the backbone of green infrastructure (GI) creation and management. In the last decades, a total of 10 000 hectares of new forests and green systems have been created. Boscoincittà and Parco Nord Milano are the best known examples of this approach aimed to redevelop the neighbourhoods of some suburbs of Milan to create multifunctional green spaces (forests, grasslands, wetlands, river corridor, and allotment gardens) in lands previously industrial or uncultivated. The creation and management of urban forests has become the backbone of GI creation and management in the Metropolitan Area of Milan. In recent decades, trends of land use change have been characterised by a rapid decrease in natural and agricultural areas and an increase in artificial and urban structures. Although the phenomenon is growing rapidly in this area, there is evidence of an opposite social and environmental trend highlighting the importance of GI positively affecting urban quality of life. Recent policies and management plans are dealing with this evidence by turning their attention to expanding green areas and infrastructure. The purpose of our investigation is to revisit effective measures designed to increase the quality and quantity of UGI in the metropolitan region under study. To this end, we assessed land use changes and described the potentialities and impacts of policies on such phenomena. The study analyses the main elements of UGI in the Italian context within the framework of the European Union Life + project called Emonfur, a research programme involving, inter alia, the establishment of an Urban Forest inventory and impact analysis of ecosystem services in the Metropolitan Area of Milan. Our research has allowed us to determine the current status of key sites by monitoring the policy and planning decisions that resulted in their development. We believe that such an analysis can pave the way to understand future land-use dynamics not only in northern Italy but in other metropolitan territories as well.

Carbon peaking and carbon neutrality strategies are pivotal in addressing climate change. Optimizing land use structure is a fundamental approach to achieving low-carbon development within a given territory. This study focuses on Fujian Province as the research subject, predicting carbon emissions for the next decade by analyzing the correlation between land use types and carbon emissions using the gray model. This analysis is based on land use panel data spanning from 2007 to 2021. The study applies the FLUS-Markov model to simulate Fujian’s land use in 2030. A multi-objective optimization model is developed from a low-carbon perspective, integrating carbon emissions, economic, and ecological factors. The study explores land use under three scenarios: natural development scenario (NS), low carbon scenario (LCS), and comprehensive scenario (CS). Findings highlight the relationship between land use-related carbon emissions, urbanization, and relevant policies in Fujian. The FLUS-Markov simulations suggest that under the NS scenario, carbon emissions in 2030 will reach 77.829 million tons, an increase of 11.013 million tons from 2020. In contrast, the LCS demonstrates that optimizing land use structures can effectively balance carbon reduction, economic growth, and ecological preservation. Under the CS, 2030 emissions could be reduced by 7.2854 million tons while maintaining economic and ecological benefits. Despite variations in construction land expansion across these scenarios, all follow a “one belt, one core” development pattern. The study concludes with policy recommendations, including industrial layout optimization and clean energy promotion. These findings support the alignment of land use optimization with Fujian’s future development needs, offering guidance for land-use planning and policies focused on low-carbon objectives.

Exploratory land use studies and their role in strategic policy making

Understanding the complex interactions (i.e., trade-offs and synergies) among ecosystem services (ESs) and exploring land use optimisation are important to realize regional ecological governance and sustainable development. This study examined Guanzhong Region, Shaanxi Province, as the research object. We established 12 future land use scenarios and projected the future land use patterns under the future climate change scenarios and local development policies. Next, we assessed the four main ecosystem services—carbon sequestration (CS), habitat quality (HQ), soil conservation (SC), and food supply (FS) by using related formulas and the InVEST model. Furthermore, the production possibility frontier (PPF) was used to measure trade-offs and synergistic relationships among ESs, and extract the optimal ES group under the different target needs. The results are as follows: (1) In the future 12 land use scenarios of 2050 in Guanzhong Region, forested land increased evidently in the RCP2.6 ecological protection scenario (18,483.64 km). In the RCP6.0 rapid urban development scenario, construction land showed evident expansion in the central and northeastern areas (4764.52 km2). (2) Compared with the ESs under the future multiple scenarios, CS and HQ achieved the maximum value in the RCP8.5 ecological protection scenario. In the RCP2.6 ecological protection scenario, the amount of SC was the largest (3.81 × 106 t). FS in the RCP2.6 business as usual scenario got the maximum value (18.53 × 106 t). (3) By drawing the optimal PPF curve of multiple scenarios in 2050, trade-off relationships were found between FS and CS, HQ, and SC, and synergistic relationships were found between CS, HQ, and SC. Next, the optimal ES groups under the fitted curve were selected by comparing with the ESs of 2018, and adjusting the land use areas and spatial pattern to finally optimise the relationships between ES and achieve the best land use spatial pattern.

A quantitative model for understanding and exploring land use decisions by smallholder agrowetland households in rural areas of East Africa

Food security in a just energy transition is a growing debate about designing sustainable food secure networks worldwide. Energy transition, land-use change, and food security are crucial factors for food security and provision. The increased demand for food products and customer preferences regarding food safety provide various issues for the current agriculture food supply chain (AFSC). Along with rising sustainability concerns, strict government regulation, food security, and traceability concerns compel managers, business houses, and practitioners working in AFSC to adopt new tools, techniques, and methodologies to model current food supply chain problems. Thus, in turn, design the food logistics network for food security. Hence, this study investigates the core determinants of food security and supply in Egypt, Morocco, Tunisia, and Lebanon over the period of 2010–2019. In order to estimate the objectives of the study, we employ the fully modified ordinary least square (FMOLS) and dynamic ordinary least squares estimators (DOLS) to draw the study findings. However, the estimated results show a negative association of land use with food security and supply. Likewise, energy transition, gross domestic product, and agricultural value added (AVA) contribute to the food security supply. In contrast, urbanization's negative but insignificant contribution to the food supply in selected economies exists. Besides, another core objective of the study is to investigate the moderate role of the energy transition on the gross domestic product, agriculture sector, and land use and find the significant contribution to the food supply. However, the current study also tries forecasting for the next 10 years and employs the impulse response function (IRF) and variance decomposition analysis (VDA). Congruently, this study uses the pairwise panel causality test and finds exciting outcomes. The COVID-19 crisis has posed challenges such as energy consumption and food security issues. On behalf of the results, the current study proposes imperative policies to investigate the desired level of food supply. The findings provide valuable insights for experts, policymakers, and officials to take practical measures for energy use and food security challenges.

This study explores land use classification in Trento using supervised learning techniques combined with call detail records (CDRs) as a proxy for human activity. Located in an alpine environment, Trento presents unique geographic challenges, including varied terrain and sparse network coverage, making it an ideal case for testing the robustness of supervised learning approaches. By analyzing spatiotemporal patterns in CDRs, we trained and evaluated several classification algorithms, including k-nearest neighbors (kNN), support vector machines (SVM), and random forests (RF), to map land use categories, such as home, work, and forest. A comparative analysis highlights the performance of each method, emphasizing the strengths of RF in capturing complex patterns, its good generalization ability, and the usage of kNN with different distance measures. Our supervised machine-learning approach outperforms unsupervised clustering techniques by capturing complex patterns and achieving higher accuracy. Results demonstrate the potential of CDRs for urban planning, offering a cost-effective approach for fine-grained land use monitoring with the particularities of Trento, as its landscape combines urban areas, agricultural fields, and forested regions, reflecting its alpine setting, in contrast with other metropolitan regions.

Land use functions can categorize places where people perform different socioeconomic activities. This classification plays an important role in urban management, policy making, and resource allocation. However, due to the rapid changes of built environment and living demands, human activities might vary significantly, in space and time, even within the same land use function as conventionally defined, impeding the formulation of targeted and user-oriented planning policies. This study took the first step to explore land use subcategorization using mobile phone-derived human activities. The study area is the 5,298 census tracts in Shanghai. Sixteen million mobile phone users’ data were collected from Shanghai Mobile Co., Ltd., in 2014. We proposed a multi-dimensional indicator framework to capture collective features of activities and identified land use subcategories using the K-Means clustering method. Analysis of variance (ANOVA) was applied to detect the proportion of activity variances captured by the classification results. Subcategory labelling method was applied to reveal the relationship between land use subcategories and built environment factors. The results show that (1) Conventional land-use functional zones (LFZs) cannot fully capture the activity variances, especially in behavioral regularity and temporal variation; (2) According to the variance analysis, at least four to five subcategories should be identified upon current LFZs to capture the main activity variances; and (3) In the case of Shanghai, land use subcategories presented palpable spatial regularity, which revealed a citywide structure deserves for further study. We concluded that data-derived activity features can provide an innovative perspective complementary to existing land use classification standards and facilitate policymakers with their decision-making processes on urban resource allocation.

The overall planning of land use is a complex process of joint action of social system, natural and economic conditions. On the basis of summarizing the existing researches, we select Shaanxi's Shangluo City, located in the Qinba mountainous area as the study object, to expound the concept and steps of scenario analysis based on land use change data, under the guidance of ecological safety and sustainable development theory. We design four different scenarios of land use planning program in Shangluo City during the period 2006-2020, and use grey linear programming model to analyze each scenario. The results show that the scenario analysis is feasible in the adjustment of land use structure in Shangluo City; operable in the determining of land use planning program on a macro-municipal scale.

This study explores land use change plans to be utilized fairly, sustainably, and resiliently for the benefit of rural community life in Tarumajaya Village, Indonesia. This study uses a qualitative method, with a case study approach to describe the land use change plan that can be utilized for the benefit of community livelihoods in Tarumajaya Village, Indonesia. This study indicates the need to increase access to land ownership for the Village government and local community. Based on field data, several alternatives can be pursued to expand the access of the community and village government to land, including utilizing part of PT. Lonsum Indofood Tbk’s land, PTPN VIII and Perhutani use change needs to be done to encourage resilient and sustainable village development innovation and realize justice and prosperity for the people of Tarumajaya Village, allocated for: (1) Agricultural Needs of the Rural Community; (2) Reserve Settlements; (3) Cemetery; (4) Health, Governance, and Education Facilities; and (5) Agricultural Areas and other productive ventures managed by the Village government. This study recommends a change of land use from PT. Lonsum Indofood Tbk, Perhutani to be managed by the Tarumajaya Village Government for the sake of sustainable and resilient village development, thus creating justice and prosperity for the community.

Most of THE SoutheastAsian countries have natural and managed ecosystems that HAVE experienced major challenges from a range of technological, environmental, socioeconomic and political forces; and these regions have been subjected to pressure of land use change, environmental degradation, water shortage, and loss of biodiversity. Food production has continued to be threatened by the scarcity of natural resources through conversion of agricultural lands caused by rapid urbanization, exploitation and competition for the use of limited resourcessuch as water. A study was conducted (1) to develop a scientific technical methodology for exploring land use options; and (2) to operationalize such methodology for supporting a network of multi-disciplinary teams and stakeholders in the ecosystem. An exploratory land use planning and analysis system (LUPAS) was developed and its component models is presented here as a case study. Optimization of the model showed a great scope for system-wide water sharing on rice-based cropping systems, generation of labor employment in agriculture, considerable increase in farmers ’ income, and the possibility of reducing soil erosion, biocides and pesticides use without adversely affecting the agricultural production in the area. LUPAS can be applied to other ecosystem areas in China, Japan and elsewhere for strategic land use planning and environmental assessment to attain food and water security.

A sound empirical and quantitative analysis on the relationship between different patterns of urban expansion and the environmental or social costs of mobility is rare, and the few studies available provide at best a qualitative discussion of these issues. Some recent tentative studies on the metropolitan area of Milan have empirically explored whether different patterns of urban expansion generate different levels of land use and heterogeneous impacts of urban mobility. The results confirmed the expectation that a higher environmental impact of mobility may result from more extensive and sprawling urban development, from recent urbanisation processes and from residential specialisation. The present paper extends the previous empirical analysis to seven major Italian metropolitan areas (namely, Bari, Florence, Naples, Padua, Perugia, Potenza and Turin) in order to corroborate the previous tentative results for the Italian context. The novelty of the present paper is threefold. First, we are interested in exploring the changes that have occurred due to the increased intensity of mobility across a ten-year period, from 1981 to 1991, which corresponds to the Italian economic boom years. Secondly, using an econometric analysis of cross-section data, we consider several metropolitan areas simultaneously, and are therefore able to explore whether there are significant differences in the way the model explains variations in the mobility impact across various Italian urban areas. And finally, we offer a structural interpretation of the causal chain in the explanation of the mobility impact intensity by using Causal Path Analysis as a statistical test framework.