Multi-objective Land Use Research Articles

Balancing Economic Growth, Carbon Emissions, and Sequestration: A Multi-Objective Spatial Optimization in Zhengzhou Metropolitan Area in China

As China’s “Dual Carbon” strategy is implemented and the new urbanization advances, balancing economic development, emission reduction, and carbon sequestration has become an important issue during the growth of emerging metropolitan areas, and it is also important for achieving high-quality urban development. Therefore, this study had three major objective functions: economic growth, carbon emission reduction, and increased carbon storage. The multi-objective land use quantity structure was solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the best solution in the solution set was introduced using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for evaluation. Finally, combined with the Future Land Use Simulation (FLUS) model, the low-carbon evolution of the metropolitan area was predicted on a spatial scale. The trade-off plan results show that by 2035, the economic benefits will reach CNY 7.65 trillion, carbon emissions will be kept under 99.24 million tons, and carbon storage will steadily increase by 15.2 million tons. Therefore, optimizing land use from the perspective of balancing carbon emissions, carbon sequestration, and economic development can provide valuable references for planning low-carbon development and the rational use of spatial resources in future metropolitan areas.

Multi-objective land use optimization based on integrated NSGA–II–PLUS model: Comprehensive consideration of economic development and ecosystem services value enhancement

Urbanization and industrialization have resulted in an excessive intensity of land use, structural imbalances, and regional ecological deterioration. To achieve sustainable development, it is essential to optimize different land use types and enhance the ecosystem service value (ESV). However, previous studies rarely combined the optimization of land use quantitative structure with the simulation of land use spatial layout under the background of maximizing economic and ecological benefits at the same time, and this kind of research was rarely applied to state-level new areas. On the premise of maximizing economic and ecological benefits, this study develops an improved optimization method that integrates the non-dominated sorting genetic algorithm II (NSGA-II) with the patch generating land use simulation (PLUS) model. The approach optimizes land use while predicting ESV for a state-level new area-Liangjiang New Area (LJNA). The model operation takes into full account ecological spatial constraints, including ecological protection red line, prime farmland, and urban development land use suitability. Results show that LJNA encountered the most substantial increase in built-up land (392.31 km2) due to farmland occupation, leading to a continuous decrease in ESV (443.6 million yuan) from 2000 to 2020. Optimization results for 2030 indicate that the main difference between different scenarios is the area of forest and built-up land. Under ecological protection priority scenario, ESV will increase in 2030 but built-up land should be confined within 620 km2. We present multiple land use scenarios based on decision makers’ preferences, offering important implications for the balance and optimization of land resources under different policy priorities using coupled spatiotemporal simulation-quantitative allocation model. This study provides valuable references for future land use optimization research.

Urban carbon emission scenario prediction and multi-objective land use optimization strategy under carbon emission constraints

With the serious challenge of persistently high carbon emissions in China, the construction of low-carbon cities is poised to become a trend in future development. However, there has been limited research quantitatively exploring carbon emission spatial targets and efficient algorithmic optimization in a systematic manner. To investigate regional carbon emission compliance and systematic optimization methods, this study takes Shanghai Pudong New Area in China as a case study. It integrates an improved Kaya identity model for forecasting, carbon emission intensity analysis, and an enhanced multi-objective genetic algorithm for land-use optimization. The study aims to predict and account for carbon emissions, compare them against carbon emission targets, analyze spatial influencing factors, and consider relevant policy constraints to simulate low-carbon land-use layouts. The findings reveal the following: (1) Through scenario forecasting and carbon emission accounting analysis, it is evident that Pudong New Area's carbon emissions do not meet the target requirements. There is potential for optimization in terms of the balance between residential and workplace spaces, land use mix, and ecological areas. (2) After applying the genetic algorithm optimization, the carbon emissions in the land-use layout plan are reduced by 11.2%–27.88 million tC compared to the original planning scheme. (3) In simulated scenarios, there are significant changes compared to planned land-use layouts, including a 49% increase in the balance between residential and workplace areas, a 20% increase in land use mix, and a 33.9% increase in per capita park green space.This systematic optimization approach effectively predicts carbon emissions, adjusts land-use layout, and achieves a reduction in carbon emissions. The conclusions provide valuable technical support for low-carbon land-use planning and offer insights for the low-carbon development of other cities."

Contribution of multi-objective land use optimization to carbon neutrality: A case study of Northwest China

Land use and land cover (LULC) patterns fundamentally influence the carbon cycle of terrestrial ecosystems, and rational optimization of LULC patterns can contribute to achieving carbon neutrality goals. This study constructs a multi-objective LULC optimization coupled model by integrating the objective of carbon neutrality into the LULC optimization process, with Northwest China serving as the study case. This model integrated the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model with the Intergovernmental Panel on Climate Change (IPCC) inventory methodology to assess the spatiotemporal variations in carbon storage and emissions in Northwest China between 2000 and 2020, which aimed to elucidate the mechanisms through which changes in LULC affect carbon neutrality. Additionally, through the amalgamation of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and the Patch-Generating Land Use Simulation (PLUS) model, optimized the quantitative structure and spatial layout of four LULC scenarios for Northwest China in 2060: natural development (ND), low carbon emissions (CE), high carbon storage (CS), and carbon neutrality (CN), projected the evolving pathways of carbon storage and emissions within these scenarios while examining the values of different LULC scenarios and their respective contributions to carbon neutrality. The study's findings underscore that the substantial expansion of built-up land development areas and farmland from 2000 to 2020 has resulted in notable carbon storage and emissions increases. Moreover, changes in carbon emissions and carbon storage show significant spatial consistency with changes in LULC patterns. In contrast to the ND scenario, the LULC patterns within the CE, CS, and CN scenarios exhibit higher LULC values and contribute more substantially to carbon neutrality. Remarkably, among these scenarios, the CN scenario stands out for achieving the maximum contribution to carbon neutrality and LULC value, thereby establishing it as the preeminent LULC pattern for the Northwest China region. As the study concludes, it recommended that future developmental strategies in Northwest China prioritize curtailing the expansion of built-up areas while bolstering the conservation of vital ecological zones such as forests and grasslands. This study addresses the inherent tension between regional economic progress and environmental preservation, offering a foundation of scientific insights for effectively managing LULC patterns and attaining carbon neutrality goals.

Spatial decision-making support for geoheritage conservation in the urban and indigenous environment of the Auckland Volcanic Field, New Zealand

Abstract It is imperative today to make geoheritage conservation an essential part of all environmental standards and operational procedures. This is because geoheritage conservation secures the preservation of in situ geoheritage elements especially in urban environments such as Auckland. Geoheritage in Auckland is strongly associated with both indigenous culture and textbook geology of monogenetic volcanism, and it can play an important role in hazard forecasting and risk mitigation. To date, there has been a lack of policy or any planning tools based explicitly on the current geopreservation inventory. Here, we present an approach to support policy making informed by a spatial multi-criteria analysis that has long been used in environmental decision-making, supported by multi-layer mapping. A systematic literature review was undertaken to define the most accepted assessment criteria used in geoheritage evaluation. We identified six criteria for the base spatial layers of our analysis, highlighting the most suitable areas for geoheritage conservation. For cultural conservation, we used available archaeological shape files, indigenous land ownership data and elevation data (the volcanic cones had multiple roles in the life of first settlers, the ancestors of the Māori). Geographic Information Systems (GIS) multi-objective land use planning is an effective procedure for achieving complex planning and preservation objectives. It allows for outcomes based on quality data and sound analysis while minimizing compromise and conflict between geoheritage, social and cultural values.

A probabilistic framework with the gradient-based method for multi-objective land use optimization

Land use planning seeks to outline the future location and type of development activity. The planning process should reconcile development with environmental conservation and other concerns pertaining to sustainability; hence multi-objective spatial optimization is considered an effective tool to serve this purpose. However, as the number of social, economic, and environmental objectives increases, especially when numerous spatial units exist, the curse of dimensionality becomes a serious problem, making previous methods unsuitable. In this paper, we formulate a probabilistic framework based on the gradient descent algorithm (GDA) to search for Pareto optimal solutions more effectively and efficiently. Under this framework, land use as decision parameter(s) in each cell is represented as a probability vector instead of an integer value. Thus, the objectives can be designed as differentiable functions such that the GDA can be used for multi-objective optimization. An initial experiment is conducted using simulation data to compare the GDA with the genetic algorithm, with the results showing that the GDA outperforms the genetic algorithm, especially for large-scale problems. Furthermore, the outcomes in a real-world case study of Shenzhen demonstrate that the proposed framework is capable of generating effective optimal scenarios more efficiently, rendering it a pragmatic tool for planning practices.

Multi-Objective Optimization of Land Use in the Beijing–Tianjin–Hebei Region of China Based on the GMOP-PLUS Coupling Model

The changeable patterns and contractions of land use have become increasingly significant in recent years as the economy and society have rapidly developed. Subsequently, land use change simulation has become a focal point in the study of land use patterns and change processes. Four development scenarios in 2030, including business-as-usual, ecological protection, economic development, and sustainable development scenarios, are proposed to realize the sustainable development of land use in Beijing–Tianjin–Hebei in the context of a low-carbon economy and ecological security. Then, a feasible multi-objective land use optimization scheme suitable for the region’s long-term development was identified through comparative analysis. The GMOP-PLUS model analyzed changes in ecological and economic benefits and carbon emissions by optimizing the quantitative structure and spatial layout of land use in different scenarios. The cultivated land area in the four scenarios decreased, while the construction land area increased for all scenarios other than the ecological protection and sustainable development scenarios. Moreover, the target development of the sustainable development scenario was the most balanced, with carbon emissions and economic benefits reduced by 49.77 million tons and CNY 0.73 billion compared with the business-as-usual scenario, respectively. Meanwhile, the ecological benefits increased by CNY 0.03 billion, and the economic benefits increased by 1.54 times compared with those in 2020. Therefore, the sustainable development scenario was more in line with the needs of Beijing, Tianjin, and Hebei for high-quality economic and ecological development, aiming towards a low-carbon goal. This work provides a theoretical basis for Beijing–Tianjin–Hebei territorial spatial planning and more perspectives for the study of sustainable land use through the obtained results.

Land Use Optimization for Coastal Urban Agglomerations Based on Economic and Ecological Gravitational Linkages and Accessibility

Urban agglomerations (UA) are attracting increasing research attention as a global emergent phenomenon, whereby regional collaborative linkages between cities attracts and agglomerates development. However, these studies also acknowledge that ecological values may be negatively impacted by re-development, ecological fragmentation, and proximity or downstream impacts. Sustainable development, therefore, requires balancing forces from economic attraction and ecological repulsion. Forces similar to economic ones may also operate in attracting ecological enhancement towards higher-valued ecological regions; however, research regarding the role of the self-collaborative gravity-like forces shaping UA is limited in land use optimization. To assist planners, this study developed a new multi-objective land use optimization of UA that explored the intensity of economic ties and ecological gradients using the multi-objective NSGA-II algorithm. In this model, economic linkage intensity (ELI) and accessibility were used to calculate a modified GDP (gross domestic product), while the NDVI (normalized difference vegetation index) was used for the modified ESV (ecosystem services value). Spatial allocation with implicit economic accessibility relationships was enhanced through a two-step mutation operator, including a “gravity flip” spatial orientation factor. Compared to the standard NSGA-II algorithm, models of future land use of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in 2030 have shown that the modified GDP value in our model increased by 7.41%, while the conversion rate of high-density vegetation reduced by 7.92%. The results highlighted the importance of linkage and accessibility factors in enhancing the clustering of cities. In tandem, the modified ESV also enhances ecosystem services contributions of higher value vegetated land through decentralized built-up developments. The proposed model provides managers with a comprehensive and efficient land use solution model that accounts for intrinsic linkage factors shaping the development of compact urban agglomerations.

Optimal Planning and Management of Land Use in River Source Region: A Case Study of Songhua River Basin, China.

Adjusting land use is a practical way to protect the ecosystem, but protecting water resources by optimizing land use is indirect and complex. The vegetation, soil, and rock affected by land use are important components of forming the water cycle and obtaining clean water sources. The focus of this study is to discuss how to optimize the demands and spatial patterns of different land use types to strengthen ecological and water resources protection more effectively. This study can also provide feasible watershed planning and policy suggestions for managers, which is conducive to the integrity of the river ecosystem and the sustainability of water resources. A watershed-scale land use planning framework integrating a hydrological model and a land use model is established. After quantifying the water retention value of land use types through a hydrological model, a multi-objective land use demands optimization model under various development scenarios is constructed. Moreover, a regional study was completed in the source area of the Songhua River in Northeast China to verify the feasibility of the framework. The results show that the method can be used to optimize land use requirements and obtain future land use maps. The water retention capacity of forestland is strong, about 2500–3000 m3/ha, and there are differences among different forest types. Planning with a single objective of economic development will expand the area of cities and cultivated land, and occupy forests, while multi-objective planning considering ecological and water source protection tends to occupy cultivated land. In the management of river headwaters, it is necessary to establish important forest reserves and strengthen the maintenance of restoration forests. Blindly expanding forest area is not an effective way to protect river headwaters. In conclusion, multi-objective land use planning can effectively balance economic development and water resources protection, and find the limits of urban expansion and key areas of ecological barriers.

Land Use Spatial Optimization Using Accessibility Maps to Integrate Land Use and Transport in Urban Areas

The scarcity of urban land resources requires a well-organized spatial layout of land use to better accommodate human activities, however, as a widely accepted concept, the integration of land use and transport is not given due consideration in land use spatial optimization (LUSO). This paper aims to integrate land use and transport in LUSO to support urban land use planning. Maximizing accessibility fitness, which follows the underlying logic between land use types and transport characteristics, is introduced into multi-objective land use spatial optimization (MOLUSO) modelling to address transport considerations, together with widely-used objectives such as maximizing compactness, compatibility, and suitability. The transport characteristics, in this study, are identified by driving accessibility, cycling accessibility, and walking accessibility. Accessibility maps, which quantify and visualize the spatial variances in accessibility fitness for different land use types, are developed based on the empirical results of the relationship between land use types and transport characteristics for LUSO and addressing policy issues. The 4-objective LUSO model and a corresponding non-dominated sorting genetic algorithm (NSGA-II) based optimization method constitute a prototype decision support system (DSS) for urban land use planning. Decision-makers (e.g., planning departments) can choose an ideal solution to accommodate urban development needs from a set of Pareto-optimal alternatives generated by the DSS. The approaches to creating accessibility maps and MOLUSO modelling are demonstrated by the case study of Eindhoven, the Netherlands. This study advocates limited changes to the current land use pattern in urban planning, and the LUSO emphasizes urban renewal and upgrading rather than new town planning.

Accuracy and validity assessment of application algorithms in land use allocation into comparison LP, SA, MOLA and MDCHOICE

The main viewpoint of land use planning is to resolve the conflict between competing land uses in one place that is carried out based on their economic, social and environmental suitability. Many different approaches have been designed to resolve this conflict but any of them have done. This study aimed to assess accuracy and validity compared four optimization algorithms, including linear programming (LP), simulated annealing (SA), multi-objective land use allocation (MOLA) and multidimensional choice (MDCHOICE) for land use planning in Gorgan Township. At first, land use allocation was done through the LP algorithm. Then, using achieved pixels quantity from LP optimization, the land uses was optimized in term of spatial suitability by other algorithms. The allocation maps generated through these algorithms were compared using statistical methods and landscape metrics. The map provided through MDCHOICE was used as a reference map and that generated through MOLA, SA and LP as a comparative one in a validation process. The similarities and agreements of the two maps were evaluated with various algorithms in IDRISI and MCK software. The results demonstrate that: (1) It follows that the LP algorithm to determine the optimal number of pixels to lead MDCHOICE has helped in the spatial allocation properly. (2) MOLA had a better performance for development in terms of all landscape metrics and SA algorithm was better in forestry land use allocation. Therefore the quality of spatial allocation both of them needs to be improved. (3) Although the LP algorithm is not superior to the algorithms based on the dimensions studied in this research, in terms of achieving the defined goals and the degree of deviation from the number of cells defined for each land use has the least violation. (4) The superiority of MDCHOICE over MOLA, SA and LP and applicability of various kappa statistics for comparing the maps for a more in depth analysis of agreements and disagreements.

Towards low-carbon cities: Patch-based multi-objective optimization of land use allocation using an improved non-dominated sorting genetic algorithm-II

The rational land use allocation is of great significance to the construction of low-carbon cities. The optimization model of land use allocation is an important tool that helps urban planners to quantitatively trade-off among the multi-objectives and achieve optimal land use schemes. For multi-objective optimization of low-carbon land use allocation, the models conducted by existing studies generally tend to be based on gridded data, lack of comprehensive consideration of quantitative and spatial objectives, and efficient algorithms to execute the optimization process. Therefore, this paper proposed a patch-based low carbon multi-objective land use allocation (LC-MLUA) optimization model involving both quantitative and spatial optimization targets. The LC-MLUA optimization model was solved with an improved non-dominated sorting genetic algorithm-II (NSGA-II), and the weighted-sum method was used to make the final selection under different preferences. The LC-MLUA optimization model was then applied to a case study of Changxing, a county-level city in east China, and there were three key results. (1) The LC-MLUA optimization model had a remarkable outperform of the land use allocation than the original land use plan, and the optimized values of economic benefit, emission reduction, and accessibility increased by 27.0%, 6.2% and 8.3%, respectively. (2) The LC-MLUA optimization model generated a series of optimal schemes to support suggestion-making for the low-carbon adjustment of the land use structure and spatial layout. (3) The LC-MLUA optimization model based on vector land patch data was proved more efficient as the unit number was reduced by 5 times than gridded data and better reflected the land use planning practice. (4) Compared with other algorithms, the improved NSGA-II had better performance in the number of solutions, target optimization rate, and comprehensive performance. Based on these results, it suggests that the patch-based LC-MLUA optimization model method can provide good technical support for low-carbon land use planning, and can be flexibly applied to other cities.

Multi-Objective Land Use Allocation Optimization in View of Overlapped Influences of Rail Transit Stations

Taking into consideration the overlapped influences of multiple rail transit stations upon land use characteristics, this study newly develops a multi-objective land use allocation optimization model to decide the land use type and intensity of every undeveloped land block of an urban area. The new model is solved by successively utilizing the non-dominated sorting genetic algorithm and the technique for order performance by similarity to ideal solution to obtain the least biased Pareto-optimal land development scheme. The study area is an urban region around two metro stations in Beijing of China. The influencing scopes of these two stations are overlapped in part, and many of the land blocks in the study area are not yet developed. It is shown that the newly developed land use allocation optimization model is able to rationally achieve multi-objectives in coordination to the most extents for the sustainable urban development in view of the integrated effect of multiple rail transit stations.

The role of indigenous practices in expanding waterbird habitat in the face of rising seas

In Hawai‘i, as is the case globally, sea level rise threatens the availability of suitable habitat for waterbirds and other coastal species. This study examines Hawaiian wetland agro-ecosystems (loʻi) as social-ecological systems that may meet human needs while expanding nesting habitat of endangered waterbirds, if restored under an Indigenous Resource Management paradigm. We applied spatial analysis to project: (1) the area of existing waterbird habitat likely lost to sea level rise by the end of the century (2100); and (2) the area of waterbird habitat potentially gained through restoration of lo‘i systems. Results show that, if loʻi offer similar or equivalent habitat value to Hawaiian waterbirds as conventionally managed wetlands, the restoration of loʻi would not only compensate for projected losses of wetland habitat due to sea level rise, but substantially contribute toward the recovery of endangered waterbirds that are currently habitat-limited. This study demonstrates capacity for contemporary Indigenous land management to address conservation and food-security needs in the Hawaiian Islands, as well as challenges of multi-objective land use and habitat restoration for endangered wetland-dependent fauna. This research further contributes toward a growing number of studies suggesting that Indigenous practices based on social-ecological frameworks offer potential to achieve sustainability and biodiversity goals simultaneously.

Optimization of multi-objective land use model with genetic algorithm

The first task of the city planner is to effectively locate integrated land use types for various objectives. The Multi Objective Land Use Planning Model developed to achieve this goal, aims to maximize land value and minimize the transportation. The genetic algorithm method developed to find the optimum layout according to the Multi-Objective Land Use Planning Model has been explained, the success and performance of the process has been tested with artificial data, and its usability in real problems has been examined. According to the results of the study, using this method, it is revealed that layout plans that are very close to the maximum efficiency value can be found within 1 day in cities with a population of up to 1,000,000, within 1 week in cities up to 5,000,000, and within 1.5 months in cities close to 16,000,000. By examining the results, the deficiencies of this method are determined and the suggestions for improvement of this method are stated. The problem chosen in this study is a problem that most city planners have to solve and the developed application has been opened to the use of other experts. This makes this work unique as it allows planning experts who are incapable of developing such methods to experiment.

Aligning Agent-Based Modeling With Multi-Objective Land-Use Allocation: Identification of Policy Gaps and Feasible Pathways to Biophysically Optimal Landscapes

It is increasingly recognized in science and policy that landscapes need to be managed for multifunctionality. Multi-objective land-use allocation and agent-based modelling are two potent tools to explore the potential of landscapes to provide multiple ecosystem services. However, in the case of the former, the real-world feasibility of the biophysically optimal land-use configurations remains unclear. Meanwhile, agent-based models are not well-suited to recognize the biophysical potential of landscapes to provide multiple ecosystem services. In this paper, we propose an approach to align multi-objective optimization with agent-based modelling in order to investigate the economic, institutional and social feasibility of biophysically optimal landscapes. It especially allows to contrast biophysically optimized land-use patterns with the option space circumscribed by relevant policy frameworks. We argue that a structured comparison of biophysical optimization with an exploration of the parameter space of an agent-based model can be used to identify the real-world feasibility and the barriers to reaching multifunctional landscapes. We demonstrate the applicability of our approach by using it on a virtual landscape, which allows us to detect the importance of various economic, institutional and behavioural factors that facilitate or hamper moving the social–ecological system towards its biophysical potential. Particularly, we demonstrate the essential role of tailored policy instruments. Our approach can be useful in informing land-use policy with respect to its effectiveness and efficiency in achieving multifunctional landscapes.

Integrated framework of system dynamics and meta-heuristic for multi-objective land use planning problem

Land use optimization as a resource allocation problem can be defined as the process of assigning different land uses to a region. Sustainable development also involves the exploitation of environmental resources, investment orientation, technology development, and industrial changes in a coordinated form. This paper studies the multi-objective sustainable land use planning problem and proposes an integrated framework, including simulation, forecasting, and optimization approaches for this problem. Land use optimization, a multifaceted process, requires complex decisions, including selection of land uses, forecasting land use allocation percentage, and assigning locations to land uses. The land use allocation percentage in the selected horizons is simulated and predicted by designing a System Dynamics (SD) model based on socio-economic variables. Furthermore, land use assignment is accomplished with a multi-objective integer programming model that is solved using augmented e-constraint and non-dominated sorting genetic algorithm II (NSGA-II) methods. According to the results of the SD model, land use changes depend on population growth rate and labor productivity variables. Among the possible scenarios, a scenario focusing more on sustainable planning is chosen and the forecasting results of this scenario are used for optimal land use allocation. The computational results show that the augmented e-constraint method cannot solve this problem even for medium sizes. The NSGA-II method not only solves the problem at large sizes over a reasonable time, but also generates good-quality solutions. NSGA-II showed better performance in metrics, including number of non-dominated Pareto solutions (NNPS), mean ideal distance (MID), and dispersion metric (DM). Integrated framework is implemented to allocate four types of land uses consisting of residential, commercial, industrial, and agricultural to a given region with 900 cells.

Spatial Multi-Objective Land Use Optimization toward Livability Based on Boundary-Based Genetic Algorithm: A Case Study in Singapore

In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have been successfully conducted, which can demonstrate the effectiveness of the spatial multi-objective land use optimization model developed in this research as well as the robustness and reliability of computer-generated solutions. In addition, the comparison between the computer-generated solutions and the two real planned scenarios has also clearly demonstrated that our generated solutions are much better in terms of fitness values. Lastly, the limitation and future direction of this research have been discussed.

Multi-Objective land use planning and modeling its change using Multi-Objective Evolutionary Algorithm based on Decomposition algorithm

Multi-Objective land use planning and modeling its change using Multi-Objective Evolutionary Algorithm based on Decomposition algorithm

Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions

Climate-smart interventions in agriculture have varying costs and environmental and economic impacts. Their implementation requires appropriate investment decisions by policy makers that are relevant for current as well as future scenarios of agro-ecology, climate and economic development. Decision support tools are therefore needed to assist different stakeholders to prioritize and hence implement appropriate strategic interventions. These interventions transform agriculture ecosystems to climate-resilient, adaptive and efficient. This paper outlines the mathematical modelling framework of one such, the Climate Smart Agricultural Prioritization (CSAP) toolkit. This toolkit employs a dynamic, spatially-explicit multi-objective optimization model to explore a range of agricultural growth pathways coupled with climate-adaptation strategies to meet agricultural development and environmental goals. The toolkit consists of three major components: (i) land evaluation including assessment of resource availability, land suitability, yield and input-output estimation for all promising crop production practices and technologies for key agro-ecological units; (ii) formulation of scenarios based on policy views and development plans; and (iii) land-use optimization in the form of linear programming models. Climate change and socio-economic drivers condition the land evaluation, technological input-output relations, and specification of optimization objectives that define modelled scenarios. By integrating detailed bottom-up biophysical, climate impact and agricultural-emissions models, CSAP is capable of supporting multi-objective analysis of agricultural production goals in relation to food self-sufficiency, incomes, employment and mitigation targets, thus supporting a wide range of analyses ranging from food security assessment to environmental impact assessment to preparation of climate smart development plans.