Search Results (28)

Flood risks are escalating globally due to unplanned urban expansion and the impacts of climate change, posing significant challenges for urban areas and necessitating effective mitigation strategies. Nature-based solutions (NBSs) have emerged as innovative and sustainable approaches for managing flood risks. The International Union for Conservation of Nature (IUCN) defines NBSs as actions that conserve, manage, and restore natural and modified ecosystems to address societal concerns while benefiting both people and the environment. This research focuses on developing NBS strategies for the most flood-prone area within Kochi, a city highly vulnerable to flooding. The study begins with a comprehensive site examination to identify flood sources and causes in Kochi, aiding in selecting flood vulnerability indicators. An analytical framework incorporating flood risk assessment and exposure studies using physical and social indicators, alongside GIS mapping techniques, revealed that approximately half of Kochi is affected. The study identified key vulnerability hotspots, particularly within the Central Business District (CBD), where high population density and inadequate infrastructure exacerbate flood risks. Proposed NBS interventions include restoring natural floodplains, enhancing canal capacities, creating urban forests, and establishing green infrastructure like permeable pavements and rainwater harvesting systems. Key findings emphasize the effectiveness of integrating NBSs with traditional flood management strategies, forming a mixed flood control system. These interventions mitigate flood risks, improve biodiversity, reduce the urban heat island effect, and enhance community well-being. Importantly, the research underscores the role of public participation and community-driven maintenance plans in ensuring the sustainability of NBS interventions. Aligning these strategies with Kochi’s Master Plan 2040 ensures coherence with broader urban planning and climate resilience goals. The research anticipates changes in climate, land use patterns, and urban dynamics to inform NBS suitability in Kochi. Ultimately, the research demonstrates how implementing NBSs can deliver a range of socio-environmental benefits, significantly influencing urban development in vulnerable zones. By advocating for the integration of NBSs into urban infrastructure planning, this study offers a blueprint for resilient and sustainable flood management strategies that are applicable to other coastal cities facing similar challenges. Full article

The application of modern technologies to increase the overall sustainability of forest operations is known as precision forest harvesting. Precision forest harvesting can be a very powerful tool; however, it requires modern forest machinery, which is expensive. Given that most of the forest operators in the Mediterranean area are small-scale businesses, they do not have the resources to purchase costly equipment; thus, the application of precision forest harvesting is affected. Bearing this in mind, in this study, we aimed to test the accuracy of the GNSS receiver on which an innovative Information and Communication Technology (ICT) system developed to monitor small-scale forest operations is based. We tested the GNSS’s accuracy by comparing the extraction routes recorded during coppicing interventions in two forest sites located in Central Italy with those obtained with a more high-performing GNSS receiver. We also used linear mixed-effects models (LMMs) to investigate the effects on the GNSS positioning error of topographic features, such as the slope, elevation, aspect and Topographic Position Index (TPI). We found that the average positioning error was about 2 m, with a maximum error of about 5 m. The LMMs showed that the investigated topographic features did not significantly affect the positioning error and that the GNSS accuracy was strongly related to the specific study area that we used as a random effect in the model (marginal coefficient of determination was about 0.13 and conditional coefficient of determination grew to about 0.59). As a consequence of the negligible canopy cover after coppicing, the tested GNSS receiver achieved satisfactory results. It could therefore be used as a visualising tool for a pre-planned extraction route network, allowing the operator to follow it on the GNSS receiver screen. However, these results are preliminary and should be further tested in more experimental sites and various operational conditions. Full article

Social-ecological interactions mediate water–energy–food security in small developing islands, but community-scale insights are underrepresented in nexus research. These interactions are dynamic in their response to environmental and anthropogenic pressures and need to be understood to inform sustainable land use planning into the future. This study centered on bringing together diverse stakeholders to explore water–energy–food futures using the “Kesho” (meaning “tomorrow” in Kiswahili) scenario tool for two of the largest islands that comprise the Zanzibar Archipelago. The methodology comprised four core stages: (1) exploration of how past drivers of change impacted water–energy–food security; (2) modeling of a Business as Usual Scenario for land cover change; (3) narrative development to describe alternative futures for 2030 based on themes developed at the community scale; and (4) predictions about how narratives would shape land cover and its implications for the nexus. These results were used to model alternate land cover scenarios in TerrSet IDRISI (v. 18.31) and produce visual representations of expected change. Findings demonstrated that deforestation, saltwater incursion, and a reduction in permanent waterbodies were projected by 2030 in a Business as Usual Scenario. Three alternative scenario narratives were developed, these included Adaptation, Ecosystem Management, and Settlement Planning. The results demonstrate that the effectiveness of actions under the scenario options differ between the islands, indicating the importance of understanding the suitability of national policies across considered scales. Synergies across the alternative scenario narratives also emerged, including integrated approaches for managing environmental change, community participation in decision making, effective protection of forests, cultural sensitivity to settlement planning, and poverty alleviation. These synergies could be used to plan strategic action towards effectively strengthening water–energy–food security in Zanzibar. Full article

The digitization of consumption, led by information and communications technology (ICT), has reshaped the urban commercial spatial structure (UCSS) of restaurants and retailers. However, the impacts of ICT on UCSS and location selection remain unclear. In this study, based on on-demand food delivery data and real-time traffic data, we used two types of machine learning algorithms, random forest regression (RFR) and the density-based spatial clustering of applications with noise (DBSCAN), to study the spatial distribution patterns, driving factors, and new geographical location phenomena of ‘brick-and-click’ (B&C) stores in Xinjiekou’s central business district (CBD) in Nanjing, China. The results show that the UCSS in the CBD is being decentralized, but the degree of influence is related to the business type. Additionally, the scale of demand and the distance from core commercial nodes greatly affect the scales of B&C stores. Moreover, the agglomeration of high-sales B&C stores seems to indicate a micro-location advantage, characterized by the concentration of delivery riders, which is usually located in the commercial hinterland with dense traffic. This makes stores situated in traditionally advantageous locations more attractive for online sales. Thus, ICT enhances the Matthew effect in business competition. These findings deepen our understanding of urban digital planning management and business systems. Full article

One of the most significant areas of local government in the world is the municipality sector. It provides various services to the residents and businesses in their areas, such as water supply, sewage disposal, healthcare, education, housing, and transport. Municipalities also promote social and economic development and ensure democratic and accountable governance. It also helps in encouraging the involvement of communities in local matters. Workers of Municipalities need to maintain their services regularly to the public. The productivity of the employees is just one of the main important factors that influence the overall organizational performance. This article compares various machine learning algorithms such as XG Boost, Random Forest (RF), Histogram Gradient Boosting Regressor, LGBM Regressor, Ada Boost Regressor, and Gradient Boosting Regressor on the dataset of municipality workers. The study aims to propose a machine learning approach to predict and evaluate the productivity of municipality workers. The evaluation of the overall targeted and actual productivity of each department shows that out of 12 different departments, only 5 departments were able to meet their targeted productivity. A 3D Scatter plot visually displays the incentive given by the department to each worker based on their productivity. The results show that XG Boost performs best in comparison with the other five algorithms, as the value of R Squared is 0.71 and MSE (Mean Squared Error) is 0.01. Full article

With the intensification of the effects of climate change, the urgent need to address their drivers, especially greenhouse gas emissions, has become essential. In this context, forests offer a robust solution, with their potential to store and mitigate carbon emissions. However, striking a balance is critical given the significant economic contribution of the forestry and wood-based industries, which account for about 5% of Romania’s GDP and employ 6% (around 300 thousand) of its active workforce. This study, conducted in the Piatra Craiului National Park located in Romania’s Southern Carpathians, we utilize the EFISCEN application to generate three distinct 50-year forest evolution scenarios based on harvest intensity, namely Business As Usual (BAU), Maximum Intensity (MAX), and No Harvest (MIN), on two historical different managed forests, i.e., conservation and production. The study aims to guide forest owners in decision making with scenario modeling tools, with the objectives of assessing the forest carbon sequestration potential and evaluating the economic feasibility. In the most probable scenario, the BAU scenario, the growing stock increases from 2.6 million m³ to 3.8 million m³ over 50 years, with a more than 40% increase. Comparing the carbon stock change for all tree harvest scenario types indicates that the MIN scenario has the highest carbon sink capacity in the next 50 years; the BAU scenario is a well-balanced option between carbon sink and wood provision and has an optimal EUR 3.7 million in annual revenue. The MAX scenario can boost the growth and increase the annual revenue from wood by 35% but is effective only for a short time and thus has the smallest calculated revenue in time. Achieving a win–win relationship between carbon sequestration and wood supply is imperative, as well as good planning and scenarios to contribute to climate mitigation and also as provisions for local communities and to sustain the local economy. Full article

Protected areas serve as stepping stones for the preservation of biodiversity, and can provide economic and social benefits to communities. National parks aim to limit human intervention to safeguard natural communities and processes. This study analyzes the impacts of transforming the Křivoklátsko Protected Landscape Area into the proposed Křivoklátsko National Park in the Czech Republic, which is a program promoted by political parties. Using the quintuple helix model, it assesses the change from a sustainable development perspective. The analysis considers economic, social, and environmental aspects, including the impact on the local inhabitants, the economy, forestry, business activities, and regional development. The existing management in the Křivoklátsko region exemplifies sustainable multifunctional forest management. Based on the evaluation, the study finds insufficient arguments for declaring the Křivoklátsko National Park. The study emphasizes the need to balance the social demand for nature protection with the awareness of existing measures and specific area conditions. Nature protection should integrate itself into all human activities within the culturally and historically created landscape, rather than solely pursuing political goals. Participatory forestry management plays a crucial role in landscape transformation. The study highlights the importance of sustainable landscape development and the interactions between the university, government, industry, and civil sector actors with the environment. Full article

Indonesia’s vast forested areas have the potential to serve as a crucial source of livelihood for local communities. However, the current contributions of these forests to community livelihoods are significantly underutilized in comparison to their potential. This study evaluates the financial performance and sustainability of community forest-based businesses operating around the forest management area of the Lakitan-Bukit Cogong forest management unit (LBC FMU). Data were collected through semi-structured and in-depth interviews with the key informants through purposive sampling. Financial viability analysis and a qualitative approach were used to assess the feasibility of the businesses. The findings revealed that all businesses show positive values for all financial indicators. From profit estimation and value-added distribution, all products are shown to be feasible. Concerning the value and supply chain, the rubber-processing industry has a remarkable flow. Moreover, small forest enterprises (SFEs) highlight natural capital optimization through multitudinous derivatives of products that could support a substantial regenerative economy, including citronella essential oil, native honeybees, rubber-based product, biochar, skewers, and liquid smoke. In addition, the multidimensional scaling and rapid appraisal for forest (MDS-RAPForest) approach generates a result based on multiple dimensions (ecology, economics, social and human resources, and institutional and administrative dimensions) showing that overall, SFEs are categorized as sufficient/quite sustainable. Furthermore, mainstreaming adaptable forest-based enterprises, jurisdiction approaches, and cross-production system strategies are also discussed. Our findings suggest that sustainable NTFP-based activities within a community context can be facilitated through interconnected market systems, appropriate price regulations, and support from stakeholders and legal frameworks. Full article

Understanding the online behavior and purchase intent of online consumers in social media can bring significant benefits to the ecommerce business and consumer research community. Despite the tight links between consumer emotions and purchase decisions, previous studies focused primarily on predicting purchase intent through web analytics and sales historical data. Here, the use of facially expressed emotions is suggested to infer the purchase intent of online consumers while watching social media video campaigns for food products (yogurt and nut butters). A FaceReader Online^TM multi-stage experiment was set, collecting data from 154 valid sessions of 74 participants. A set of different classification models was deployed, and the performance evaluation metrics were compared. The models included Neural Networks (NNs), Logistic Regression (LR), Decision Trees (DTs), Random Forest (RF,) and Support Vector Machine (SVM). The NNs proved highly accurate (90–91%) in predicting the consumers’ intention to buy or try the product, while RF showed promising results (75%). The expressions of sadness and surprise indicated the highest levels of relative importance in RF and DTs correspondingly. Despite the low activation scores in arousal, micro expressions of emotions proved to be sufficient input in predicting purchase intent based on instances of facially decoded emotions. Full article

With the development of the Internet and communication technologies, the types of services provided by multitier Web systems are becoming more diverse and complex compared to those of the past. Ensuring a continuous availability of business services is crucial for multitier Web system providers, as service performance issues immediately affect customer experience and satisfaction. Large companies attempt to monitor the system performance indicator (SPI) that summarizes the status of multitier Web systems to detect performance anomalies at an early stage. However, the current anomaly detection methods are designed to monitor a single specific SPI. Moreover, the existing approaches consider performance anomaly detection and its root cause analysis separately, thereby aggravating the burden of resolving the performance anomaly. To support the system provider in diagnosing the performance anomaly, we propose an advanced causative metric analysis (ACMA) framework. First, we draw out 191 performance metrics (PMs) closely related to the target SPI. Among these PMs, the ACMA determines 62 vital PMs that have the most influence on the variance of the target SPI using several statistical methods. Then, we implement a performance anomaly detection model to identify the causative metrics (CMs) between the vital PMs using random forest regression. Even if the target SPI changes, our detection model does not require any change in its model structure and can derive closely related PMs of the target SPI. Based on our experiments, wherein we applied the ACMA to the business services in an enterprise system, we observed that the proposed ACMA could correctly detect various performance anomalies and their CMs. Full article

Bioenergy represents a viable renewable alternative for the many off-grid remote communities in Northern Canada that rely on diesel-based energy infrastructure. Despite the abundance of forest-based biomass, bioenergy for heat and power in Canada is used primarily in industrial contexts. Community-scale bioenergy, although growing, has been limited. Supply chain challenges, institutional and policy arrangements, and community perspectives indicate a need to better understand the ‘business ecosystem’ for bioenergy in Canada. The ecosystem includes technologies, community contexts, suppliers, developers, and policy makers. In this study, we explore the bioenergy business ecosystem challenges and perspectives from supply-side stakeholders. Interviews were conducted with representatives from the government, industry, and community—all working in bioenergy. The results indicate the following challenges facing the bioenergy ecosystem, with respect to community energy security: lack of cross-jurisdictional consistency in legislation and policies across Canada, structural issues such as subsidized energy and utility ownership, and misdirected support for local capacity building in the bioenergy sector. We also find that the existing support systems are prone to misuse, pointing to efficiency gaps in investment flows. The insights that emerge from this work, especially from industry stakeholders, are meaningful for communities and policy makers alike. Full article

Understanding the evolution of land use/land cover change (LULCC) and how it shapes current and future ecosystem services (ES) supply potential remains critical in sustainable natural resource management. Community perception of historic LULCC was reconciled with previous study via remote sensing/geographical information systems using recall data in the Budongo–Bugoma landscape in Uganda. Then, a CA-Markovian prediction model of a LULC situation in 2040 under business as usual (BAU) and forest restoration scenarios was constructed. Additionally, we assessed the perceived proximate and underlying drivers of LULCC, and how LULCC shapes ecosystem services potential using household surveys. The perceived LULCC trend for the past three decades (1990–2020) corresponded with previous studies showing grassland, bushland, tropical high forest, and wetland cover declined greatly, while subsistence farmland, commercial farmland, and built-up areas had a great increment. The predicted LULC under (i) the business as usual scenario showed a continued decline of natural LULC while anthropogenic LULC increased greatly, tending to cover half of the landscape area; (ii) forest restoration under different levels showed an improvement of forest cover and other native LULC classes with a decline in mostly subsistence farmland. The proximate drivers were in three principal components (soil infertility, subsistence farming, drought; infrastructural development, commercial farming, overstocking of livestock, pest and disease challenges; tree planting), while underlying drivers were in two principal components (technology adoption, corruption of environment stewards, policy implementation gaps; cultural gaps). Food and cash crops were perceived to be the most important ecosystem services in the landscape. Generally, the landscape ES supply potential was dwindling and predicted to continue with a similar trend under BAU, despite the increment in ES contribution of subsistence and commercial farmland. Forest restoration would slightly improve the landscape ES potential but would cause a decline in subsistence farmland, which would result in either a threat to food/livelihood security or a livelihood shift. We recommend combined interventions that seek to achieve a progressive frontier that achieves development needs and priorities based on national need such as food security through local level production with recognition for sustainable availability of ecosystem services. Full article

This study examines land use changes and evaluates the past and projected forest carbon sequestration and its valuation in Viti Levu Island, Fiji, through a combination of remote sensing with a geospatial-based modeling approach. Land use classification was performed using Landsat 7 and Landsat 8 imageries of the years 2000 and 2020; then, cellular automata and artificial neural network (CA-ANN) modeling was conducted to predict the land use map of 2040. Carbon sequestration and the economic valuation were estimated using the land use maps of the past, present, and future (2000, 2020, and 2040) within the Integrated Valuation of Ecosystems Trade-off (InVEST) model. The results showed that deforestation occurred during the past two decades, and the forest area was predicted to keep decreasing in 2040, with the major contribution from the conversion to the agricultural area. Local communities’ perceptions confirmed that the forest conversion to croplands would persist due to the demand for fertile lands. This study estimated a loss of −7.337 megatonnes of forest carbon (Mt C) with an economic loss of USD −1369.38 million during 2000–2020 due to deforestation. If the business-as-usual scenario does not change in the near future, a potential carbon loss of −7.959 Mt C is predicted in the upcoming 20 years. The predicted results can be used to assist as a reference in establishing a national baseline and reference level for implementing the REDD+ mechanism in Fiji and sustainably managing the limited pristine forest by implementing forest-related programs. Full article

Prunus mume has been cultivated for more than three millennia with important edible, ornamental, and medicinal value. Due to its sour taste, the Prunus mume fruit (called Meizi in Chinese and Ume in Japanese) is not very popular compared to other fruits. It is, however, a very favorite food for the Bai people living in Eryuan County, Dali of Yunnan, China. The local people are masters of making various local products with plum in different ways. In this research, we conducted field investigations in Eryuan County using ethnobotanical methods from August 2019 to July 2021, focusing on the Prunus mume (for its edible fruits). A total of 76 key informants participated in our semi-structured interviews. The survey recorded 37 species (and varieties) belonging to 11 families related to the Bai people’s Meizi-consuming culture. Among them, there are 14 taxa of plum resources, including one original species and 13 varieties. These 37 species are either used as substitutes for plum due to their similar taste or as seasonings to improve the sour taste of plum. The higher Cultural Food Significance Index value implies that Prunus mume, Chaenomeles speciosa, Phyllanthus emblica, Prunus salicina, and Chaenomeles cathayensis have high acceptance and use value in the Bai communities. Among the various local products traditionally made by the Bai people, carved plums, preserved plums, perilla-wrapped plums, and stewed plums are the most famous and popular categories in the traditional markets. Currently, the plum business based on the traditional Meizi-consuming culture of the Bai people is already one of Eryuan’s economic pillars. This study showed that plums play an important role in expressing the local cultural diversity, and they also help the local people by improving their livelihood through their edible value. In turn, for the sustainable use of plum resources, the Bai people positively manage local forests through a series of measures to protect the diversity of plum resources and related plant communities. Full article

Over the past couple of decades, many telecommunication industries have passed through the different facets of the digital revolution by integrating artificial intelligence (AI) techniques into the way they run and define their processes. Relevant data acquisition, analysis, harnessing, and mining are now fully considered vital drivers for business growth in these industries. Machine learning, a subset of artificial intelligence (AI), can assist, particularly in learning patterns in big data chunks, intelligent extrapolative extraction of data and automatic decision-making in predictive learning. Firstly, in this paper, a detailed performance benchmarking of adaptive learning capacities of different key machine-learning-based regression models is provided for extrapolative analysis of throughput data acquired at the different user communication distances to the gNodeB transmitter in 5G new radio networks. Secondly, a random forest (RF)-based machine learning model combined with a least-squares boosting algorithm and Bayesian hyperparameter tuning method for further extrapolative analysis of the acquired throughput data is proposed. The proposed model is herein referred to as the RF-LS-BPT method. While the least-squares boosting algorithm is engaged to turn the possible RF weak learners to form stronger ones, resulting in a single strong prediction model, the Bayesian hyperparameter tuning automatically determines the best RF hyperparameter values, thereby enabling the proposed RF-LS-BPT model to obtain desired optimal prediction performance. The application of the proposed RF-LS-BPT method showed superior prediction accuracy over the ordinary random forest model and six other machine-learning-based regression models on the acquired throughput data. The coefficient of determination (Rsq) and mean absolute error (MAE) values obtained for the throughput prediction at different user locations using the proposed RF-LS-BPT method range from 0.9800 to 0.9999 and 0.42 to 4.24, respectively. The standard RF models attained 0.9644 to 0.9944 Rsq and 5.47 to 12.56 MAE values. The improved throughput prediction accuracy of the proposed RF-LS-BPT method demonstrates the significance of hyperparameter tuning/optimization in developing precise and reliable machine-learning-based regression models. The projected model would find valuable applications in throughput estimation and modeling in 5G and beyond 5G wireless communication systems. Full article