Hasil untuk "Land use"

J. Muñoz‐Sabater, E. Dutra, A. Agustí-Panareda et al.

Abstract. Framed within the Copernicus Climate Change Service of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the 5th generation of European ReAnalysis (ERA5), hereafter named as ERA5-Land. Once completed, the period covered will span from 1950 to present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, enabling the characterisation of trends and anomalies. This is achieved through global high resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5 most of the parametrizations that guarantees the use of the state-of-the-art land surface modeling applied to Numerical Weather Prediction (NWP) models. A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution is hourly as in ERA5. Evaluation against independent in situ observations and global model or satellite-based reference datasets shows the added value of ERA5-Land in the description of the hydrological cycle, in particular with enhanced soil moisture and lake description, and an overall better agreement of river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed behaviour when compared to those of ERA5, depending on geographical location and altitude. The description of the energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as reference MODIS data, mainly due to the contribution of coastal points where spatial resolution is important. Since January 2020, the ERA5-Land period available extends from January 1981 to near present, with 2 to 3 months delay with respect to real-time. The segment prior to 1981 is in production, aiming to a release of the whole dataset in summer 2021. The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies, to initialise NWP and climate models, and to support diverse applications dealing with water resource, land and environmental management. The full ERA5-Land hourly and monthly averaged dataset presented in this paper are available through the Climate Data Store, https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30, respectively.

Yan-sui Liu, Yan-sui Liu, Fang Fang et al.

S. Sohi

A. Don, J. Schumacher, A. Freibauer

K. K. Goldewijk, A. Beusen, G. Drecht et al.

G. Hurtt, G. Hurtt, L. Chini et al.

In preparation for the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), the international community is developing new advanced Earth System Models (ESMs) to assess the combined effects of human activities (e.g. land use and fossil fuel emissions) on the carbon-climate system. In addition, four Representative Concentration Pathway (RCP) scenarios of the future (2005–2100) are being provided by four Integrated Assessment Model (IAM) teams to be used as input to the ESMs for future carbon-climate projections (Moss et al. 2010). The diversity of approaches and requirements among IAMs and ESMs for tracking land-use change, along with the dependence of model projections on land-use history, presents a challenge for effectively passing data between these communities and for smoothly transitioning from the historical estimates to future projections. Here, a harmonized set of land-use scenarios are presented that smoothly connects historical reconstructions of land use with future projections, in the format required by ESMs. The land-use harmonization strategy estimates fractional land-use patterns and underlying land-use transitions annually for the time period 1500–2100 at 0.5° × 0.5° resolution. Inputs include new gridded historical maps of crop and pasture data from HYDE 3.1 for 1500–2005, updated estimates of historical national wood harvest and of shifting cultivation, and future information on crop, pasture, and wood harvest from the IAM implementations of the RCPs for the period 2005–2100. The computational method integrates these multiple data sources, while minimizing differences at the transition between the historical reconstruction ending conditions and IAM initial conditions, and working to preserve the future changes depicted by the IAMs at the grid cell level. This study for the first time harmonizes land-use history data together with future scenario information from multiple IAMs into a single consistent, spatially gridded, set of land-use change scenarios for studies of human impacts on the past, present, and future Earth system.

E. Allan, P. Manning, F. Alt et al.

Abstract Global change, especially land‐use intensification, affects human well‐being by impacting the delivery of multiple ecosystem services (multifunctionality). However, whether biodiversity loss is a major component of global change effects on multifunctionality in real‐world ecosystems, as in experimental ones, remains unclear. Therefore, we assessed biodiversity, functional composition and 14 ecosystem services on 150 agricultural grasslands differing in land‐use intensity. We also introduce five multifunctionality measures in which ecosystem services were weighted according to realistic land‐use objectives. We found that indirect land‐use effects, i.e. those mediated by biodiversity loss and by changes to functional composition, were as strong as direct effects on average. Their strength varied with land‐use objectives and regional context. Biodiversity loss explained indirect effects in a region of intermediate productivity and was most damaging when land‐use objectives favoured supporting and cultural services. In contrast, functional composition shifts, towards fast‐growing plant species, strongly increased provisioning services in more inherently unproductive grasslands.

Xiaoling Chen, Hongrui Zhao, Pingxiang Li et al.

P. Verburg, W. Soepboer, A. Veldkamp et al.

M. Halmy, P. Gessler, J. Hicke et al.

Xiaoqiang Wan, Chengyu Wang, Quanlai Ma et al.

To investigate the characteristics of soil heavy metal pollution in the Manghe River watershed, a typical industrial and mining complex area in the Yellow River Basin, concentrations of Hg, Cr, Cu, Ni, Pb, Zn, Cd, and pH were measured in 121 topsoil samples (0-20 cm) collected from the study area. Geostatistical methods were employed to analyze the spatial distribution patterns of heavy metals. The pollution status was assessed using the pollution load index (PLI), while correlation analysis, principal component analysis (PCA), and a positive matrix factorization (PMF) model were applied to identify the sources of heavy metals. The results indicated that: (1) The concentrations of Hg, As, Ni, Cu, Pb, Zn, and Cd exceeded their respective background values, with Hg, Pb and Cd reaching 3.52, 4.85, and 46.4 times of the background levels, respectively.(2) Different elements exhibited distinct spatial distribution and diffusion patterns, revealing their respective sources and influencing factors. (3) The overall PLI was 0.785, reflecting a mild pollution level across the region, while industrial and mining lands exhibited severe pollution (PLI = 4.3). The relative contribution of each heavy metal to the pollution load was ranked as follows: Cd (30.35)> Pb (4.76)> Hg (3.62)> Zn (2.18)> As (1.77)> Cu (1.53). (4) Principal component analysis categorized the sources of heavy metals into anthropogenic activities and natural origins. Further analysis using the PMF model delineated four specific sources: coal combustion (10.87%), natural and agricultural contributions (27.37%), transportation and agricultural actives (26.81%), and industrial emissions (34.95%). Finally, the study identified the following feasible strategies for controlling heavy metal pollution: blocking and remediating industrial pollution sources; treating agricultural non-point source pollution through biological methods; and substituting traditional transportation sources with new energy alternatives. This research could support decision-making processes related to the prevention and control of heavy metal pollution in the study area, as well as regional sustainable development.

Yanlin Wang, Shuo Yan, Shanshan Gao et al.

The distribution of suitable habitats for medicinal plants is affected by climate, soil, land use, and other factors. <i>Arnebiae Radix</i>, an important traditional Chinese medicinal resource in Xinjiang, includes <i>Arnebia euchroma</i> (Royle) I. M. Johnst. and <i>Arnebia guttata</i> Bunge and is at risk of over-exploitation. This study predicted suitable planting areas by integrating habitat and phytochemical suitability using the MaxEnt model and ArcGIS. The AUC values for <i>A. euchroma</i> and <i>A. guttata</i> were 0.977 and 0.952, with TSS values of 0.829 and 0.725, respectively, validating the high accuracy of the prediction model. Under the current scenario, the areas of suitable habitats for <i>A. euchroma</i> and <i>A. guttata</i> were 108,914 and 176,445 km², mainly distributed along the main mountains in Xinjiang. Under future climate scenarios, the suitable habitat area of <i>A. euchroma</i> increased by 11–18%, except in the ssp126-2090s scenario, while the suitable habitat area of <i>A. guttata</i> area decreased by 3–18%. Both species were influenced by land use/land cover and soil available nitrogen content; additionally, <i>A. euchroma</i> was affected by the precipitation in the driest month, and <i>A. guttata</i> by the mean diurnal range. The content of secondary metabolites was positively correlated with habitat suitability, with soil factors contributing 35.25% to the total secondary metabolite content. Their suitable habitats predominantly occur in grasslands (42–82%). As habitat and phytochemical suitability distributions aligned, the eastern and western sides of the northern Kunlun Mountain Pass emerged as key areas for cultivation. This research can provide a scientific foundation for selecting optimal planting regions for the two <i>Arnebia</i> species.

Simone Figorilli, Lavinia Moscovini, Simone Vasta et al.

Recently banana plantations have been affected by the Black Sigatoka Disease (BSD), producing streaks, lesions and yellow and brown spots on the leaves until the appearance of entire dead parts. The disease causes reductions in yield making it essential to assess infection by monitoring plants status and implementing agronomical measures. This work aims to develop a physical field device to identify the BSD presence. It consists in a 3D printed prototype embedding a smartphone acquiring and processing banana leaves images. An advanced Artificial Intelligence model was trained and implemented for real-time processing. The algorithm is a Convolutional Neural Network (CNN) able to classify the samples into 6 classes representative of different BSD stages infection. The trained model, showing an accuracy of 82 % in training and 78 % in validation, was integrated into a specifically developed mobile application for field use. The Android app allows to acquire, identify the georeferenced infection stage, sync all to a remote dedicated host from which the results can be mapped and exported to a .csv file for easy data management. The distinction between healthy and diseased leaves can be achieved using the Smart BSD device for real-time acquisition, establishing the right intervention strategy.

Karine Sartelet, Jules Kerckhoffs, Eleni Athanasopoulou et al.

Mapping urban pollution is essential for assessing population exposure and addressing associated health impacts. High urban concentrations are due to the proximity of sources such as traffic or residential heating, and to urban density with the presence of buildings that reduce street ventilation. This urban complexity makes fine-scale mapping challenging, even for regulated pollutants such as NO2 and PM2.5. In this study we apply state-of-the-art empirical and deterministic modeling approaches to produce high-resolution (<100 m) pollution maps across five European cities (Paris, Athens, Birmingham, Rotterdam, Bucharest). These methodologies enable full-city mapping capturing intra-urban gradients of concentrations. Depending on the methodology, regulated pollutants (NO2, PM2.5) and/or emerging pollutants (black carbon (BC) and ultrafine particles (UFP characterized here by particulate number concentration PNC)) are considered. For deterministic modelling, different approaches are presented: a multi-scale Eulerian modelling chain down to the street scale with chemistry/aerosol dynamics at all scales, multi-scale hybrid models with Eulerian regional dispersion and Gaussian subgrid dispersion, and a Gaussian-based model. Empirical land use regression models were developed based upon mobile monitoring.To compare the relative performance of the methodologies and to evaluate their performance and limitations, the modelling results are compared to fixed measurement stations. We introduce a standardized metric to quantify spatial and seasonal variability and assess each method’s capacity to reproduce fine-scale urban heterogeneity. We also evaluate how data assimilation affects both concentration accuracy and variability representation—particularly relevant for emerging pollutants where measurement data are sparse. We confirm established seasonal and spatial patterns: spatial variability is more pronounced for PNC, NO2 and BC than PM2.5, and concentrations are higher during the winter periods. We also observe reduced spatial variability in winter for PM2. 5 (linked to residential heating) and for BC in cities with significant wood burning emissions. This study adds unique value by evaluating these patterns using fixed measurement stations, and quantifying them across entire urban areas at very fine spatial resolution (<100 m). Furthermore, important methodological strengths and limitations are pointed out, providing practical guidance for the selection and improvement of urban exposure mapping methods, supporting the implementation of the new EU Air Quality Directive.

Klenam Korbla Ledi

This study proposes mechanisms for firms to thrive in a turbulent business environment by investigating the mediating role of frugal innovation in the relationship between competitive intelligence and value creation. The study also examined absorptive capacity as a critical boundary condition that influences the impact of competitive intelligence on frugal innovation. A survey questionnaire was administered to 302 SMEs, and the data were analysed using Amos. Structural equation modelling (SEM) was employed to test the hypothesis. The findings demonstrate a significant positive impact of competitive intelligence on SME value creation. The study also found that frugal innovation acts as a mechanism to amplify the impact of competitive intelligence on value creation, and this relationship is anchored by a high level of absorptive capacity. The study demonstrates how competitive intelligence and frugal innovation synergistically drive value creation amid turbulence under the auspices of absorptive capacity, thereby increasing SMEs’ resilience and competitiveness. The study sheds light on previously unexplored aspects of value creation in a turbulent business environment, offering novel perspectives on dynamic capabilities.

Joshi Rutuparna, Zhu Ming

Progressively, geosynthetics have been used in combination with and in lieu of natural materials to solve complex geotechnical problems while offering a drastic reduction in carbon emissions and minimizing environmental impacts. Commonly used final cover systems for landfills and containment areas include soil-only, soil-geosynthetic, engineered turf cover (ETC), and evapotranspiration (ET) cover system. This presentation provides a detailed review of various sustainability aspects of a geosynthetic ETC including comparisons with traditional covers. Sustainability aspects considered include carbon emissions; deforestation, land use change and borrow areas; land, soil and water conservation; run-off water quality and downstream impacts; reduction in fugitive landfill gas emissions; and beneficial reuse opportunities.

Shoaib Khan, Ameen Qasem

AbstractThis study examines the empirical relationship between the different leverage levels as a proxy of financing mix on the financial performance of the non-financial firms listed on capital markets in GCC economies. The study uses the pooled ordinary least squares regression (OLS), fixed and random effects regression, and feasible generalised least square (FGLS) regression to explore the relationship among variables on the data of GCC firms listed from 2011 to 2021. The results suggest that the capital structure considerably affects firms’ performance. Findings refute the theoretical assumptions of Modigliani and Miller’s debt irrelevance and debt-supporting theorem. The findings also contradict the debt-supporting benefits the agency and trade-off theory suggest. Empirically, short-term, long-term, and total debt adversely affect the return on assets, equity, and earnings per share. Control variables, growth opportunities, and size of the firm positively and asset tangibility negatively contribute to the performance. The results will support the managers in making performance-improving financing decisions. Lenders should improve ex-ante screening and ex-post monitoring to avoid possible defaults. Local and foreign investors should carefully examine the firms’ debt levels before making investment decisions. Policymakers should focus on the flourishing of the bond markets to support privatisation and economic diversification. Our study is the first to use the recent data of GCC-listed firms to examine the impact of capital structure on firms’ performance. Contributing to the literature gap will also lay a foundation for a more comparative study on corporate financing with alternative financial instruments.

Adia Bey, A. S. Díaz, D. Maniatis et al.

Collect Earth is a free and open source software for land monitoring developed by the Food and Agriculture Organization of the United Nations (FAO). Built on Google desktop and cloud computing technologies, Collect Earth facilitates access to multiple freely available archives of satellite imagery, including archives with very high spatial resolution imagery (Google Earth, Bing Maps) and those with very high temporal resolution imagery (e.g., Google Earth Engine, Google Earth Engine Code Editor). Collectively, these archives offer free access to an unparalleled amount of information on current and past land dynamics for any location in the world. Collect Earth draws upon these archives and the synergies of imagery of multiple resolutions to enable an innovative method for land monitoring that we present here: augmented visual interpretation. In this study, we provide a full overview of Collect Earth’s structure and functionality, and we present the methodology used to undertake land monitoring through augmented visual interpretation. To illustrate the application of the tool and its customization potential, an example of land monitoring in Papua New Guinea (PNG) is presented. The PNG example demonstrates that Collect Earth is a comprehensive and user-friendly tool for land monitoring and that it has the potential to be used to assess land use, land use change, natural disasters, sustainable management of scarce resources and ecosystem functioning. By enabling non-remote sensing experts to assess more than 100 sites per day, we believe that Collect Earth can be used to rapidly and sustainably build capacity for land monitoring and to substantively improve our collective understanding of the world’s land use and land cover.

Devyn Remme, Siddharth Sareen, Håvard Haarstad et al.

Norway has the world-class ambition to make transport more sustainable and climate friendly. Its electric vehicle (EV) rollout is celebrated by and aspirational for other countries, manifesting the imaginary of technological solutions for sustainable mobility. This chapter undertakes a critically constructive analysis of the value chains of this rollout, tracing the production, usage and discard of EVs. Our point of departure in Norway’s EV rollout serves to map broader implications of a rapid, massive shift towards electric transport. We map relevant externalities associated with, for example, the mining of raw materials and with modes of digitalisation that run counter to circular economy principles. The requisite resources for the transition to renewably powered, electrified transportation―notably batteries―are sourced in the global South, whereas their consumption and industries that reuse and recycle valuable minerals are emerging in the global North. The uneven distribution of benefits and burdens is increasingly being criticised as green extractivism for an imperial mode of living. By paying attention to site-specific struggles over resources, our mapping demonstrates that practices of legitimation have yet to be welded with holistic accountability. By piecing together some major links along the value chains of Norway’s EV rollout, we argue for a global perspective on this transition.

Abdalwali Lutfi, Ahmad Farhan Alshira’h, Malek Hamed Alshirah et al.

Despite tax being a fundamental method to redistribute wealth and achieve a sustainable economic and social system, tax agencies and institutions in most countries are struggling with low tax collections. This issue is often attributed to the level of compliance among taxpayers. To gain more insight into this problem, a study was conducted to examine how socio-economic determinants such as probability of detection, tax complexity, tax penalty, tax sanctions, tax ethics, tax justice, government spending, and tax services quality impact VAT compliance decisions. The study drew a random sample of 770 retail industry participants from Jordan, an Arabic country, for a self-administered survey. Smart-PLS structural equation modeling was used to analyze and estimate the compliance model. The results indicated that all proposed direct relationships were supported, and the interactions between tax knowledge and the socio-economic determinants on VAT compliance were found to be significant. The findings of this research can be useful for policymakers and institutions responsible for taxpayers' communities to understand the role of tax knowledge in VAT compliance in the retail industry. The study emphasizes the significance of instilling tax knowledge, social and moral values among VAT payers, establishing an equitable system, and launching awareness programs in Jordanian society. Additionally, it contributes to existing literature by confirming a practical compliance model rooted in the socio-economic theory of regulatory compliance. This model incorporates the moderating effect of tax knowledge within socio-economic aspects of VAT compliance. By understanding the importance of tax knowledge, policymakers and institutions can develop effective strategies to boost VAT funds and improve compliance in the retail industry. This can ultimately lead to increased government revenues without placing an undue economic burden on lower-income taxpayers.