Land Use Allocation Research Articles

A multi-objective optimization framework for regional land-use allocation: Fully utilizing terrestrial vegetation to mitigate carbon emissions

Human-induced changes in land use and land cover (LULC) considerably impact the global carbon cycle. Rational LULC optimization with full utilization of terrestrial vegetation facilitates reconciling carbon reduction and socio-economic development. To this end, this study develops a multi-objective framework for low-carbon LULC spatial optimization that incorporates carbon suitability, historical suitability, and socio-economic needs. The framework is applied to Hubei Province, China. The key findings include: (1) Considering spatial variations in vegetation carbon sinks could lead to remarkable carbon reductions of 2.75 × 106 t C by 2030 and 7.68 × 106 t C by 2060. It would also lower carbon emissions per unit of gross domestic product (GDP) and enhance carbon sequestration per unit of vegetation by 2030, signifying enhanced carbon reduction efficiency. (2) Integrating carbon sequestration potential and historical suitability can considerably minimize emissions while sustaining socio-economic growth. Our method, compared with strategies based solely on historical development laws, can reduce carbon emissions by approximately 1.3–1.6 × 106 t C·a−1. (3) We also propose targeted low-carbon development suggestions for different LULCs. This study provides optimal spatial LULC allocations and strategic advice for regional LULC planning aimed at low-carbon development, demonstrating the feasibility of reconciling the tension between carbon reduction and socio-economic growth at the regional scale.

The spatial planning of housing and urban green space: A combined stated choice experiment and land-use modeling approach

Urban green space (UGS) is receiving increasing attention as a means to make cities better adapted to climate change and to create high-quality living environments for citizens. The spatial planning of UGS generally asks a trade-off between optimizing land-use and accessibility characteristics which may differ between housing types. In this paper, we use a stated choice experiment to estimate residential location preferences related to neighborhood land-use, UGS and accessibility characteristics in the framework of a land-use model. A national sample of 394 Dutch homeowners participated in the experiments and mixed logit models were estimated by housing type distinguishing detached, row houses and apartments. The estimation results show that trade-offs made between a green neighborhood and accessibility to urban amenities tend to differ between the housing types. The estimates are used as parameters in a housing land-use allocation model. An application of the model to a residential area development problem shows that creating green buffers along road infrastructure is the most beneficial way of allocating UGS considering the housing value for residents. The application further shows that apartments benefit more strongly from UGS in the neighborhood than from high accessibility of urban amenities compared to detached and row houses. Finally, we find that the optimal spatial allocation of UGS depends on whether maximizing housing market-value or residents’ utility is the prime objective.

Multi-actor approach to manage the water-energy-food nexus at territory scale

This study presents an innovative water-energy-food nexus (WEFN) generic multi-actor multi-level framework that simulates dynamic nexus systems and generates optimal resource management schemes and land-use allocation scenarios. The framework combines operational integrated multi-agent based simulations, dynamic agro-hydrological and renewable energy generation units (solar and wind) models and multi-criteria decision-making methods. It has been applied to a case study area of 800 km2 located downstream the Aveyron watershed, in Southwestern France. Local and regional strategic multi-criteria decision-making settings are compared. Under the local decision-making setting land-use allocation decisions are taken at agricultural parcel level whereas under the regional decision-making settings a single land allocation decision is taken for the whole territory. Possible land allocations are either crop rotations allocations, renewable energy generation units allocations or a mix of both. The framework is useful to generate scenarios of spatial allocations of renewable energy production units in an agricultural watershed facing water scarcity.

The Use of an Optimized Grey Multi-Objective Programming-PLUS Model for Multi-Scenario Simulation of Land Use in the Weigan–Kuche River Oasis, China

The oasis serves as the primary supply of cultivable land, along with the hub for human production and habitation in Xinjiang. Accordingly, predicting the land use of these areas based on various goals is an effective instrument for encouraging the sensible distribution of resource space. The study investigated the creation of a land use-allocation optimization model based on the various objectives of ecological protection, food security, and urban growth using the Weigan–Kuche River oasis as an example. The GMOP-PLUS model’s restriction conversion area was adjusted to include the findings of the land suitability evaluation. Additionally, it optimized and simulated the spatial arrangement and quantitative structure of land usage in the Weigan–Kuche River oasis in 2035. The results indicate the following: (1) the model’s overall accuracy is 89.36%, and its Kappa coefficient is 0.872, more than 0.8. Thus, the model can be considered for adoption in the future when predicting changes in land use in the districts and counties of the Weigan–Kuche River oasis; (2) based on the results of the land suitability evaluation, the percentage of areas that are most suited for agricultural development, urban development, and ecological protection is 39.32%, 24.21%, and 14.06%, respectively; and (3) the three scenarios satisfy the various demands for growth within the oasis, and the land use structure of the oasis varies considerably in response to the various development objectives, with the construction and cultivated land undergoing the most substantial modifications. The multi-scenario simulation of land usage in the oasis can provide essential support and a range of perspectives for future land spatial planning and socioeconomic development decision-making in the Weigan–Kuche River oasis. This is essential for both the efficient use of land resources and sustainable development.

Large differences between observed and expected Ecuadorian deforestation from 2001 to 2009: a counterfactual simulation approach

Ensuring the integrity of the world’s forests is indispensable for mitigating climate change, combatting biodiversity loss, and protecting the livelihoods of rural communities. While many strategies have been developed to address deforestation across different geographic scales, measuring their impact against a fluctuating background of market-driven forest loss is notoriously challenging. In this article, we (1) asses deforestation in Ecuador using a dynamic, counterfactual baseline that excludes non-market factors, (2) identify periods of reduced and excess deforestation, and (3) assess the economic consequences of associated CO2 emissions using the social cost of carbon metric. We construct a counterfactual market-forces-only reference scenario by simulating heterogeneous profit-seeking agents making satisficing land-use allocation decisions under uncertainty. The model simulates a reference scenario for 2001–2022, a period encompassing dollarization, the beginning of a constitution granting inalienable rights to nature, and the launch of the largest payments for ecosystem services program in Ecuador’s history. On this period, total deforestation was approximately 20% lower than expected in a market-forces-only scenario (9540 vs.12,000 km2). The largest deviation occurred in 2001–2009, when observed deforestation was 43.6% lower than expected (3720 vs 6590 km2). From 2010 onwards, deforestation appears to be market-driven. We assess the economic value of avoided CO2 emissions at US $5.7 billion if the reduction is permanent, or US $3.1 billion considering a 1% risk of loss from 2022 onwards. We discuss contributing factors that likely shaped periods of reduced and excess deforestation and stress the need to use realistic baselines.

Risk reduction through spatial plan: A case study from Surabaya, Indonesia

Spatial planning has a vital role in mitigating and reducing risks by discouraging settlements and other forms of development in hazard-prone areas. Integrating spatial planning and disaster risk reduction is crucial to achieving resilient cities. Selecting Surabaya City in Indonesia as a case study, this research uses a questionnaire, in-depth interview, and documentary study to evaluate the performance of Surabaya spatial planning documents on risk reduction. This paper aims to formulate substantial steps to integrate risk reduction in Indonesian spatial planning based on evaluation to Surabaya spatial plans. The result shows that the performance of Surabaya spatial plans on average is scored 2.26 out of 5, indicating that the integration of disaster risk reduction on the Surabaya spatial planning document is still low. In response to this performance gap, eight policy recommendations are formulated to improve the future Indonesian spatial plan. In conclusion, the policy recommendations for future Indonesian spatial planning in making resilient cities are; conducting comprehensive research on risk management, mainstreaming risk reduction in development policies, limiting urban development in hazard-prone areas, providing adequate risk reduction infrastructures and emergency infrastructures, considering both hazard and risk maps for land-use allocation, making a detailed land-use control based on varieties of relevant hazards, and enacting clear incentives and disincentives mechanism related to risk reduction.

Economic development and optimal allocation of land use in ecological emigration area in China

Due to factors such as climate, environment, and resource scarcity, a large number of ecological immigration activities have been carried out in many regions of the world, and a large amount of land has been abandoned in emigration areas, which makes it difficult to reflect the economic value of the land in the process of land ecological restoration. Many scholars mainly focus on the socio-economic development of the immigration area, while the ecological economic value of the land in the emigration area is neglected. The Chinese government has organised and implemented six large-scale resettlement projects in Ningxia to alleviate poverty and bring prosperity to areas where the soil and water are not well nourished and people are struggling. Thus, this study observed Guyuan City, Haiyuan County, and Tongxin County in central and southern Ningxia and conducted a systematic and comprehensive analysis of land resources, natural resources, the ecological environment, and social economy in the emigration area. Based on the accurate identification of the land scale of the emigration area, evaluating industrial suitability in the ecological emigration area, this study intended to strike a balance between ecological and economic development. The results showed that the emigration area is 8.0794 million mus, and the main types of land use are grassland, arable land, and forest land; the industrial development directions include large-scale livestock farming, drought-tolerant fruit farming, cold-resistant vegetable farming, modern forestry, specialised understory and photovoltaic and wind power production. The results provide policy inspiration for sustainable land use in ecological emigration areas, and propose practical ways for social capital and financial institutions to participate in the land development and promote ecological and economic value balance of emigration areas in the world.

Balancing urbanization, agricultural production and ecological integrity: A cross-scale landscape functional and structural approach in China

Projecting land-use allocation under various scenarios can be useful for reconciling conflicts between urbanization, food security, and ecological integrity throughout landscapes, but existing approaches often fail to capture the interactions in land systems that occur across scales and within and between hierarchical jurisdictions of societal organization. This article introduces a cross-scale functional and structural approach for balancing trade-offs in landscape planning and sustainable land management at multiple levels, based on the principles of multifunctional suitability. Using Heilongjiang Province in China as a case, we simulate four landscape scenarios that progressively consider cross-scale and inter-regional trade-offs more broadly, as well as local suitability. In the business-as-usual (BAU) scenario, no multifunctionality objectives are considered at any level; in the own-provincial scale (OPS) scenario, intra-province multifunctionality objectives are considered; the national and provincial scales (NPS) scenario considers multifunctionality within and beyond the province; while the desired land-use (DLU) scenario balances cross-scale multifunctionality while also optimizing for local functional suitability. Promoting multifunctionality as desirable, and evaluated based on minimizing functional land loss, the DLU scenario significantly outperforms the BAU scenario. The results indicate that the DLU scenario outperforms BAU, OPL, and NPS in optimizing urban and cropland layout, and preserving ecosystem integrity. It reduces high-quality arable land loss by 6.5% and minimizes the impact on ecological function areas by 12%, surpassing the BAU scenario. In contrast to the OPS and NPS scenarios, the DLU scenario exhibits a 6% and 2% increase in population and economic agglomeration within the Harbin-Daqing-Suihua (HDS) city cluster, a pivotal component of the national urbanization strategy. Furthermore, it enhances grain production capacity in the national grain-producing area, the Sanjiang Plain (SJP), with 9% and 12% boosts, respectively. Additionally, the DLU scenario expands the ecologically significant area, showing a 3.6% and 1% improvement in the Greater and Lesser Khingan Mountains (GLKM), nationally vital ecological service function areas. These advantages of the DLU scenario are expected to become more pronounced over time. The novel theoretical approach and modelling framework introduced in this study can serve as inspiration elsewhere for reconciling future land-use trade-offs and planning decisions across scales.

Transformation scenarios towards multifunctional landscapes: A multi-criteria land-use allocation model applied to Jambi Province, Indonesia

In tropical regions, shifting from forests and traditional agroforestry to intensive plantations generates conflicts between human welfare (farmers’ demands and societal needs) and environmental protection. Achieving sustainability in this transformation will inevitably involve trade-offs between multiple ecological and socioeconomic functions. To address these trade-offs, our study used a new methodological approach allowing the identification of transformation scenarios, including theoretical landscape compositions that satisfy multiple ecological functions (i.e., structural complexity, microclimatic conditions, organic carbon in plant biomass, soil organic carbon and nutrient leaching losses), and farmers needs (i.e., labor and input requirements, total income to land, and return to land and labor) while accounting for the uncertain provision of these functions and having an actual potential for adoption by farmers. We combined a robust, multi-objective optimization approach with an iterative search algorithm allowing the identification of ecological and socioeconomic functions that best explain current land-use decisions. The model then optimized the theoretical land-use composition that satisfied multiple ecological and socioeconomic functions. Between these ends, we simulated transformation scenarios reflecting the transition from current land-use composition towards a normative multifunctional optimum. These transformation scenarios involve increasing the number of optimized socioeconomic or ecological functions, leading to higher functional richness (i.e., number of functions). We applied this method to smallholder farms in the Jambi Province, Indonesia, where traditional rubber agroforestry, rubber plantations, and oil palm plantations are the main land-use systems. Given the currently practiced land-use systems, our study revealed short-term returns to land as the principal factor in explaining current land-use decisions. Fostering an alternative composition that satisfies additional socioeconomic functions would require minor changes (“low-hanging fruits”). However, satisfying even a single ecological indicator (e.g., reduction of nutrient leaching losses) would demand substantial changes in the current land-use composition (“moonshot”). This would inevitably lead to a profit decline, underscoring the need for incentives if the societal goal is to establish multifunctional agricultural landscapes. With many oil palm plantations nearing the end of their production cycles in the Jambi province, there is a unique window of opportunity to transform agricultural landscapes.

Sustainable and Resilient Land Use Planning: A Multi-Objective Optimization Approach

Land use allocation (LUA) is of prime importance for the development of urban sustainability and resilience. Since the process of planning and managing land use requires balancing different conflicting social, economic, and environmental factors, it has become a complex and significant issue in urban planning worldwide. LUA is usually regarded as a spatial multi-objective optimization (MOO) problem in previous studies. In this paper, we develop an MOO approach for tackling the LUA problem, in which maximum economy, minimum carbon emissions, maximum accessibility, maximum integration, and maximum compactness are formulated as optimal objectives. To solve the MOO problem, an improved non-dominated sorting genetic algorithm III (NSGA-III) is proposed in terms of mutation and crossover operations by preserving the constraints on the sizes for each land use type. The proposed approach was applied to KaMavota district, Maputo City, Mozambique, to generate a proper land use plan. The results showed that the improved NSGA-III yielded better performance than the standard NSGA-III. The optimal solutions produced by the MOO approach provide good trade-offs between the conflicting objectives. This research is beneficial for policymakers and city planners by providing alternative land use allocation plans for urban sustainability and resilience.

Impact of forest landscape restoration in combating soil erosion in the Lake Abaya catchment, Southern Ethiopia

As an effect of forest degradation, soil erosion is among Ethiopia’s most pressing environmental challenges and a major threat to food security where it could potentially compromise the ecosystem functions and services. As the effects of soil erosion intensify, the landscape’s capacity to support ecosystem functions and services is compromised. Exploring the ecological implications of soil erosion is crucial. This study investigated the soil loss and land degradation in the Lake Abaya catchment to explore forest landscape restoration (FLR) implementation as a possible countermeasure to the effects. The study used a geographic information system (GIS)-based approach of the Revised Universal Soil Loss Equation (RUSLE) to determine the potential annual soil loss and develop an erosion risk map. Results show that 13% of the catchment, which accounts for approximately 110,000 ha, is under high erosion risk of exceeding the average annual tolerable soil loss of 10 t/ha/year. Allocation of land on steep slopes to crop production is the major reason for the calculated high erosion risk in the catchment. A scenario-based analysis was implemented following the slope-based land-use allocation proposal indicated in the Rural Land Use Proclamation 456/2005 of Ethiopia. The scenario analysis resulted in a reversal erosion effect whereby an estimated 3000 t/ha/year of soil loss in the catchment. Thus, FLR activities hold great potential for minimizing soil loss and contributing to supporting functioning and providing ecosystem services. Tree-based agroforestry systems are among the key FLR measures championed in highly degraded landscapes in Ethiopia. This study helps policymakers and FLR implementors identify erosion risk areas for future FLR activities. Thereby, it contributes to achieving the country’s restoration commitment.

Optimal allocation system of urban land use types under multi-objective constraints

Abstract This paper arranges suitable land use types according to the quality characteristics of land parcel units, obtains the objective function of optimal land use allocation, and constructs a multi-objective constraint system in the process of land use planning. The integer vector is used to encode the land use types and represent the spatial distribution of land use, combined with the particle swarm algorithm to calculate its adaptability, speed and position update based on the initial population size and construct the land use optimization allocation model based on the constraint system. The particle swarm algorithm is used to explore the geographic environment and climate resources, the economy of the natural economy of the study area, and Landsat TM remote sensing image technology is used to obtain the initial data on the land use of the study area and empirical analysis is carried out on the optimization of the allocation of urban land use in the study area. The results show that the algorithm converges faster and achieves global convergence when the parameter is set to the fourth state, and the comprehensive suitability evaluation function achieves global convergence at generation 73, with a value of 1.64E+08, which is faster than the convergence of parameter settings 1 and 2 as well as 3. The land types that increase in area include garden land, forest land, transportation land, and water and water conservancy facilities land, which increase by 0.8%, 2.84%, 0.14%, and 0.8%, respectively, which protects the arable land and restricts the expansion of the construction land in terms of the quantitative structure. This study enriches the content and methods of optimizing land use space allocation, which helps to rationalize the use of land resources.

Land-Use Optimization Based on Ecological Security Pattern—A Case Study of Baicheng, Northeast China

In the current context of global urbanization and climate change, balancing ecological protection and economic development is a particular challenge in the optimal allocation of regional land use. Here, we propose a research framework for the optimal allocation of land use that considers the regional ecological security pattern (ESP) and allocates space for land-use activities to areas with low ecological risk. Taking Baicheng, China as our study area, ecological sources were first identified by integrating their ecological importance and landscape connectivity, and ecological corridors and functional zones were extracted using the minimum cumulative resistance difference and circuit theory. The ecological source areas were then taken as limiting factors, and four future scenarios were established for 2030 using the parcel-level land-use simulator (PLUS) model. The ecological corridors and functional zones served as areas having restricted ecological conditions, and the four future scenarios were coupled into the corresponding functional zones to optimize the land-use structure in 2030. The results indicate that under the coupled ESP–PLUS scenario, the spatial distribution and structure of land use in Baicheng balance the needs of ecological source area protection and economic development, resulting in greater sustainability. By 2030, the cultivated land area will steadily increase, but attention will also be given to the protection of ecological land (e.g., woodland and marshland), aligning with current policy planning demands. An analysis of the landscape indices for each future scenario found all scenarios to be effective in reducing negative changes in landscape patterns. These findings provide a novel perspective for the rational allocation of future land resources and the optimization of land-use structures.

Benefits and trade-offs of optimizing global land use for food, water, and carbon

Current large-scale patterns of land use reflect history, local traditions, and production costs, much more so than they reflect biophysical potential or global supply and demand for food and freshwater, or-more recently-climate change mitigation. We quantified alternative land-use allocations that consider trade-offs for these demands by combining a dynamic vegetation model and an optimization algorithm to determine Pareto-optimal land-use allocations under changing climate conditions in 2090-2099 and alternatively in 2033-2042. These form the outer bounds of the option space for global land-use transformation. Results show a potential to increase all three indicators (+83% in crop production, +8% in available runoff, and +3% in carbon storage globally) compared to the current land-use configuration, with clear land-use priority areas: Tropical and boreal forests were preserved, crops were produced in temperate regions, and pastures were preferentially allocated in semiarid grasslands and savannas. Transformations toward optimal land-use patterns would imply extensive reconfigurations and changes in land management, but the required annual land-use changes were nevertheless of similar magnitude as those suggested by established land-use change scenarios. The optimization results clearly show that large benefits could be achieved when land use is reconsidered under a "global supply" perspective with a regional focus that differs across the world's regions in order to achieve the supply of key ecosystem services under the emerging global pressures.

Exploring the Coupling Coordination and Key Factors between Urban–Rural Integrated Development and Land-Use Efficiency in the Yellow River Basin

Exploring the complex dynamic relationship between urban–rural integrated development and land-use efficiency can contribute to most efficient urban–rural land-use and the rational promotion of urban–rural integrated development. This study established an evaluation model of urban–rural integrated development, adopted the super-efficiency SBM model to measure land-use efficiency, and studied the evolution of the spatial–temporal patterns of urban–rural integrated development and land-use efficiency coupling in the Yellow River Basin. We also examined the factors affecting them with the help of the coupling coordination degree model, non-parametric kernel density estimation, and geographic probes. The results indicate the following: (1) Within the study period, the coupled coordination of urban–rural integrated development and land-use efficiency was similar to the spatial distribution characteristics of land-use efficiency, both showing a “high at both ends and low in the middle” trend. (2) The coupled coordination increased over time; however, a lagging land-use efficiency was a crucial impediment to improving the coupling coordination degree. (3) Carbon emissions, urbanization rate, and per capita GDP were key drivers. The results of this study can provide a reference for local governments in the Yellow River Basin and other similar areas to propose paths to optimize the allocation of urban and rural land-use.

Climate-smart peatland management and the potential for synergies between food security and climate change objectives in Indonesia

Tropical peatlands lie at a nexus of competing sustainable development demands of enhancing food security, mitigating climate change, improving resilience and supporting rural livelihoods. Meeting United Nations Sustainable Development Goals (SDGs) requires balancing these various demands. Progress in meeting SDGs has been slow in low to middle income countries because of difficulties in identifying and quantifying the trade offs associated with natural resource exploitation, including on extensive areas of tropical peatlands.Here, by using secondary data from the literature, Indonesian and international agencies, we examine how land-use allocation in Indonesia has developed over the last three decades by investigating trends of key food and woody crops (oil palm and rubber) and evaluate the role that peatland provinces have played in food security and climate forcing. Overall, food crop production has been marginal in peatland provinces compared to monoculture woody crops, with the latter associated with increased carbon emissions from land use, land-use change, and forestry (LULUCF) over the last thirty years. Our analysis shows that synergies between responses to looming food security and climate change crises can also promote less damaging forms of tropical peatland management. For instance, the conversion of degraded shallow peatlands to agroecological practices (e.g., paludiculture) can be promoted. However, we stress that peatland conservation and restoration must remain the top priority. Impediments due to lack of a common definition for peatland and planning/management units, the use of multiple sectoral maps by different government agencies and uncoordinated sectoral policy targets can, however, hinder the implementation of less damaging peatland management.

Energy analysis in Water-Energy-Food-Carbon Nexus

This study evaluated the comprehensive Water-Energy-Food-Carbon Nexus (WEFC) by focusing on energy assessment in northwest Iran. The energy evaluation indices for different products were calculated by estimating the total input and output energies. Multi-objective optimization based on five individual objectives and WEFC Nexus policies was used to identify the optimal land-use allocation of wheat, barley, rapeseed, and sugar beet, silage corn, and potato while minimizing water and energy consumption and CO2 emissions, and maximizing food production and profit. The results indicate that the suggested framework provides a practical methodology for determining the optimal land-use allocation considering quantitative WEFC Nexus. To increase economic efficiency and reduce energy consumption, agricultural practices and policy recommendations should be adopted, including promoting renewable energy sources, implementing energy-saving technologies, improving fertilizer management, improving crop rotation practices, conservation tillage, and improving water management and adoption of sustainable farming practices. The results allow policymakers to optimize multiple resources and recommend the best resource allocation under recommendation policy, technology, and constraints to achieve sustainable development in agriculture.

Trends in tropical forest loss and the social value of emission reductions

Reducing global forest losses is essential to mitigate climate change and its associated social costs. Multiple market and non-market factors can enhance or reduce forest loss. Here, to understand the role of non-market factors (for example, policies, climate anomalies or conflicts), we can compare observed trends to a reference (expected) scenario that excludes non-market factors. We define an expected scenario by simulating land-use decisions solely driven by market prices, productivities and presumably plausible decision-making. The land-use allocation model considers economic profits and uncertainties as incentives for forest conversion. We compare reference forest losses in Brazil, the Democratic Republic of Congo and Indonesia (2000–2019) with observed forest losses and assign differences from non-market factors. Our results suggest that non-market factors temporarily lead to lower-than-expected forest losses summing to 11.1 million hectares, but also to phases with higher-than-expected forest losses of 11.3 million hectares. Phases with lower-than-expected forest losses occurred earlier than those with higher-than-expected forest losses. The damages avoided by delaying emissions that would otherwise have occurred represent a social value of US$61.6 billion (as of the year 2000). This result shows the economic importance of forest conservation efforts in the tropics, even if reduced forest loss might be temporary and reverse over time.

Optimal Modeling of Sustainable Land Use Planning under Uncertain at a Watershed Level: Interval Stochastic Fuzzy Linear Programming with Chance Constraints

In this paper, an uncertain interval stochastic fuzzy chance constraint land use optimal allocation method is proposed and applied to solve the problem of land use planning in river basins. The UISFCL-LUP method is an aggregation of interval parametric programming, fuzzy linear programming and chance constraint programming which can cope with uncertain problems such as interval value, fuzzy set and probability. In this paper, the uncertain mathematical method is explored and studied in the optimal allocation of land use in the next two planning periods of Nansihu Lake Basin in China. Moreover, it was proved that ISFCL-LUP can deal with the uncertainty of interval, membership function and probability representation and can also be used to solve the land use planning and land use strategy analysis under uncertain conditions. On the basis of model calculations, we obtained the optimal allocation results for six types of land use in four regions over two planning periods based on different environmental constraints. The results show that the optimized λ value (that is, the degree of satisfaction with all the model conditions) is in the range of [0.54, 0.79] and the corresponding system benefits are between [18.4, 20.4] × 1012 RMB and [96.7, 109.3] × 1012 RMB. The results indicate that land managers can make judgments based on the different socio-economic development needs of different regions and determine strategic land use allocation plans under uncertain conditions. At the same time, the model obtained interval solutions under different system satisfaction and constraint violation probabilities, which helps land managers to analyze the importance of land system optimization and sustainable development more deeply.

Artificial intelligence for sustainable development of smart cities and urban land-use management

ABSTRACT The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas. This problem is an NP (nondeterministic polynomial time)-hard problem because of involving many objective functions, many constraints, and complex search space. Moreover, this subject is an important issue in smart cities and newly developed areas of cities to achieve a sustainable arrangement of land-uses. Different types ofMulti-Objective Optimization Algorithms (MOOAs) based on Artificial Intelligence (AI) have been frequently employed, but their ability and performance have not been evaluated and compared properly. This paper aims to employ and compare three commonly used MOOAs i.e. NSGA-II, MOPSO, and MOEA/D in urban land-use allocation problems. Selected algorithms belong to different categories of MOOAs family to investigate their advantage and disadvantages. The objective functions of this study are compatibility, dependency, suitability, and compactness of land-uses and the constraint is compensating of Per-Capita demand in the urban environment. Evaluation of results is based on the dispersion of the solutions, diversity of the solutions’ space, and comparing the number of dominant solutions in Pareto-Fronts. The results showed that all three algorithms improved the objective functions related to the current arrangement of the land-uses. However, the run time of NSGA-II is the worst, related to the Diversity Metric (DM) which represents the regularity of the distance between solutions at the highest degree. Moreover, MOPSO provides the best Scattering Diversity Metric (SDM) which shows the diversity of solutions in the solution space. Furthermore, In terms of algorithm execution time, MOEA/D performed better than the other two. So, Decision-makers should consider different aspects in choosing the appropriate MOOA for land-use management problems.