A GIS-Assisted Assessment and Attribute-Based Clustering of Forest Wetland Utility in South Korea

Urban greening in Dhaka: Assessing rooftop agriculture suitability using GIS and MCDM techniques

Dhaka ranks among the world's most densely populated cities, with built-up areas expanding to accommodate the demands of a growing population. The rapid urbanization has reduced green space and exacerbated urban heat and pollution in the city. In the quest for a greener and healthier urban environment, rooftop agriculture has emerged as a promising solution, offering opportunities for the restoration of the environment and safe food production. Despite its potential, limited studies have explored the viability of this alternative greening solution for Dhaka. Therefore, this study aims to assess the suitability of rooftops for agricultural activities employing Geographic Information System (GIS) and Multi-Criteria Decision Making (MCDM) techniques. First, seven criteria were selected based on the literature, such as building age, height, rooftop size, building utility, property value, sunlight, and water availability. Second, an expert opinion survey was conducted using the Best Worst Method (BWM) to calculate the criteria's weights. Finally, the suitability map for Dhaka was derived by combining the criteria layers and was subsequently validated. Rooftop area and property value were identified as the most and least important criteria. Approximately 9% (6.27 km2), 68% (46.59 km2), 22% (15.15 km2), and a negligible portion (0.1 km2) of Dhaka city has been classified as highly suitable, suitable, moderately suitable, and not suitable, respectively, for rooftop agriculture. By identifying and promoting the most suitable locations for rooftop agriculture and highlighting existing opportunities, this research will help to initiate and expand sustainable agriculture practices that can contribute to climate change adaptation and urban resilience.

Cluster Analysis of the EU-27 Countries in Light of the Guiding Principles of the European Green Deal, with Particular Emphasis on Poland

The article presents a cluster analysis of the EU-27 countries. The clusters were built to identify groups of countries similar to each other in relation to the set of Eurostat indicators from the Climate Change Drivers and Environment and Energy sections. During the research, tools of spatial information systems were used, such as cluster analysis, diagram maps, rasterization and the TSA method. ARIMA prediction models were also used. The research aims to verify our hypotheses. Particular attention was paid to Poland; therefore, it was verified whether the composition of the country’s energy mix translated into excessive emissions of pollutants in relation to other EU countries. Furthermore, the level of integration of energy markets in the European Union and its changes over time were examined. The authors also proposed a methodology to create detailed energy and climate strategies for designated clusters. The results of the presented research are particularly important in light of recent events in Ukraine.

Methods of Ensuring Energy Security with the Use of Hard Coal—The Case of Poland

In this article, the authors present methods based on hard coal that may ensure energy security for European Union countries. The research was carried out based on the example of Poland. The main reason for which coal is being gradually withdrawn from the energy mixes in EU countries is its negative impact on the natural environment and the health of citizens and economic factors related to domestic fuel production. The authors propose the creation of energy–chemical clusters as a solution to these problems. It is assumed that the clusters would operate following the principles of the circular economy. We also propose methods for the optimization of the production and transport costs within the cluster. Then, we conduct profitability analysis of the proposed waste management methods. At the level of the designated cluster, using network algorithms enabled us to reduce the transport costs by at least 50%. It is possible to obtain rare earth elements (REEs) worth USD 22,970 from 1 Mg of ash. At the level of the analyzed cluster, this leads to an annual profit of USD 3.5 billion. The profit related to algae production at the cluster level is approximately USD 2.5 bn.

Dealing with Water Conflicts: A Comprehensive Review of MCDM Approaches to Manage Freshwater Ecosystem Services

This paper presents a comprehensive review of the application of Multiple-Criteria Decision-Making (MCDM) approaches exclusively to water-related freshwater ecosystem services. MCDM analysis has been useful in solving conflicts and it works well in this framework, given the serious conflicts historically associated with water use and the protection of freshwater ecosystems around the world. In this study, we present a review of 150 papers that proposed the use of MCDM-based methods for the social, economic, or ecological planning and management of water ecosystem services over the period 2000–2020. The analysis accounts for six elements: ecosystem service type, method, participation, biogeographical realm, waterbody type, and problem to solve. A Chi-square test was used to identify dependence between these elements. Studies involving the participation of stakeholder groups adopted an integrated approach to analysing sustainable water management, considering provisioning, regulating, and cultural services. However, such studies have been in decline since 2015, in favour of non-participatory studies that were strictly focused on ecological and provisioning issues. Although this reflects greater concern for the health of freshwater ecosystems, it is a long way removed from the essence of ecosystem services, which entails an integrated approach to the interrelationships between hydrology, landscapes, ecology, and humans.

Identification and Prediction of Wetland Ecological Risk in Key Cities of the Yangtze River Economic Belt: From the Perspective of Land Development

Rapid urbanization aggravates the degradation of wetland function. However, few studies have quantitatively analyzed and predicted the comprehensive impacts of different scenarios and types of human activities on wetland ecosystems from the perspective of land development. Combined with the Habitat Risk Assessment (HRA) model and the Cellular Automata (Ca)-Markov model, this study quantitatively measured the impact intensity and spatial distribution of different types of human activities on the wetland ecosystem in 2015, simulated and predicted the ecological pressure on the wetland in 2030, and identified the ecological risk hotspots of the Yangtze River waterfront along the upper, middle, and lower reaches of the Yangtze River Economic Belt. The results showed that the ecological risk of wetlands in the study area was low in the urban core and high in the suburbs. Construction activities posed a greater risk to wetlands. The intensity of human activities in the ecological protection scenario will be significantly lower than that in the natural development scenario in 2030. The waterfront in the middle and lower reaches of the Yangtze River will face more ecological risks. The results of the study can provide theoretical and technical support for wetland conservation policy formulation and waterfront development in the Yangtze River Economic Belt.

Optimization of the Weighted Linear Combination Method for Agricultural Land Suitability Evaluation Considering Current Land Use and Regional Differences

This study was aimed at optimizing the weighted linear combination method (WLC) for agricultural land suitability evaluation (ALSE) through indicator selection, weight determination, and classification of overall suitability scores in Handan, China. Handan is a representative research area with distinct agricultural advantages and regional differences in land use, where the expansion of construction land has led to a rapid decrease of agricultural land in recent years. Natural factors (topography, climate, soil conditions, and vegetation cover) and socioeconomic factors (land use and spatial accessibility) were selected to establish a more comprehensive evaluation system. The index weight was calculated by the mutual information between index suitability and current land use. The consistency index was used to identify the boundary value dividing the overall suitability score into a suitable category and unsuitable category in each sub-region. The results demonstrated that the optimized WLC-ALSE model outperformed the comparison models using conventional methods in terms of the consistency between the evaluation results and current land use. Owing to the increasing limitations of topography, soil conditions, spatial accessibility, and land use, the proportions of suitable land in Zone 1, Zone 2, and Zone 3 were 77.4%, 67.5%, and 30.9%, respectively. The agricultural land unsuitable for agriculture (14.5%) was less than non-agricultural land suitable for agriculture (7.4%), indicating that agricultural land had low growth potential in Handan. Finally, specific recommendations were made to improve agricultural land suitability, alleviate land use conflicts, and further optimize the model. The results can provide effective guidance for WLC-ALSE and land use decision-making for sustainable agriculture.

Optimal Soybean (Glycine max L.) Land Suitability Using GIS-Based Multicriteria Analysis and Sentinel-2 Multitemporal Images

Soybean is regarded as one of the most produced crops in the world, presenting a source of high-quality protein for human and animal diets. The general objective of the study was to determine the optimal soybean land suitability and conduct its mapping based on the multicriteria analysis. The multicriteria analysis was based on Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) integration, using Sentinel-2 multitemporal images for suitability validation. The study area covered Osijek-Baranja County, a 4155 km2 area located in eastern Croatia. Three criteria standardization methods (fuzzy, stepwise and linear) were evaluated for soybean land suitability calculation. The delineation of soybean land suitability classes was performed by k-means unsupervised classification. An independent accuracy assessment of calculated suitability values was performed by a novel approach with peak Normalized Difference Vegetation Index (NDVI) values, derived from four Sentinel-2 multispectral satellite images. Fuzzy standardization with the combination of soil and climate criteria produced the most accurate suitability values, having the top coefficient of determination of 0.8438. A total of 14.5% of the study area (602 km2) was determined as the most suitable class for soybean cultivation based on k-means classification results, while 64.3% resulted in some degree of suitability.