Hasil untuk "Land use"

T. Newbold, Lawrence N. Hudson, Andy Arnell et al.

Crossing “safe” limits for biodiversity loss The planetary boundaries framework attempts to set limits for biodiversity loss within which ecological function is relatively unaffected. Newbold et al. present a quantitative global analysis of the extent to which the proposed planetary boundary has been crossed (see the Perspective by Oliver). Using over 2 million records for nearly 40,000 terrestrial species, they modeled the response of biodiversity to land use and related pressures and then estimated, at a spatial resolution of ∼1 km2, the extent and spatial patterns of changes in local biodiversity. Across 65% of the terrestrial surface, land use and related pressures have caused biotic intactness to decline beyond 10%, the proposed “safe” planetary boundary. Changes have been most pronounced in grassland biomes and biodiversity hotspots. Science, this issue p. 288; see also p. 220 Land use has reduced biosphere intactness below safe limits across 65% of Earth’s terrestrial surface, especially in grasslands. Land use and related pressures have reduced local terrestrial biodiversity, but it is unclear how the magnitude of change relates to the recently proposed planetary boundary (“safe limit”). We estimate that land use and related pressures have already reduced local biodiversity intactness—the average proportion of natural biodiversity remaining in local ecosystems—beyond its recently proposed planetary boundary across 58.1% of the world’s land surface, where 71.4% of the human population live. Biodiversity intactness within most biomes (especially grassland biomes), most biodiversity hotspots, and even some wilderness areas is inferred to be beyond the boundary. Such widespread transgression of safe limits suggests that biodiversity loss, if unchecked, will undermine efforts toward long-term sustainable development.

L. Aleksandrowicz, Rosemary Green, E. Joy et al.

Food production is a major driver of greenhouse gas (GHG) emissions, water and land use, and dietary risk factors are contributors to non-communicable diseases. Shifts in dietary patterns can therefore potentially provide benefits for both the environment and health. However, there is uncertainty about the magnitude of these impacts, and the dietary changes necessary to achieve them. We systematically review the evidence on changes in GHG emissions, land use, and water use, from shifting current dietary intakes to environmentally sustainable dietary patterns. We find 14 common sustainable dietary patterns across reviewed studies, with reductions as high as 70–80% of GHG emissions and land use, and 50% of water use (with medians of about 20–30% for these indicators across all studies) possible by adopting sustainable dietary patterns. Reductions in environmental footprints were generally proportional to the magnitude of animal-based food restriction. Dietary shifts also yielded modest benefits in all-cause mortality risk. Our review reveals that environmental and health benefits are possible by shifting current Western diets to a variety of more sustainable dietary patterns.

L. B. Guo, R. Gifford

E. Kalnay, M. Cai

R. Houghton, A. Nassikas

A. C. Guzha, M. Rufino, S. Okoth et al.

Abstract Region East Africa. Focus A review of catchment studies (n = 37) conducted in East Africa evaluating the impacts of Land Use and Land Cover Changes (LULCC) on discharge, surface runoff, and low flows. New hydrological insights Forest cover loss is accompanied by increased stream discharges and surface runoff. No significant difference in stream discharge is observed between bamboo and pine plantation catchments, and between cultivated and tea plantation catchments. Trend analyses show that despite forest cover loss, 63% of the watersheds show non-significant changes in annual discharges while 31% show increasing trends. Half of the watersheds show non-significant trends in wet season flows and low flows while 35% reveal decreasing trends in low flows. Modeling studies estimate that forest cover loss increases annual discharges and surface runoff by 16 ± 5.5% and 45 ± 14%, respectively. Peak flows increased by a mean of 10 ± 2.8% while low flows decreased by a mean of 7 ± 5.3%. Increased forest cover decreases annual discharges and surface runoff by 13 ± 1.9% and 25 ± 5%, respectively. Weak correlations between forest cover and runoff (r = 0.42, p

T. Newbold

Land-use and climate change are among the greatest threats facing biodiversity, but understanding their combined effects has been hampered by modelling and data limitations, resulting in part from the very different scales at which land-use and climate processes operate. I combine two different modelling paradigms to predict the separate and combined (additive) effects of climate and land-use change on terrestrial vertebrate communities under four different scenarios. I predict that climate-change effects are likely to become a major pressure on biodiversity in the coming decades, probably matching or exceeding the effects of land-use change by 2070. The combined effects of both pressures are predicted to lead to an average cumulative loss of 37.9% of species from vertebrate communities under ‘business as usual’ (uncertainty ranging from 15.7% to 54.2%). Areas that are predicted to experience the effects of both pressures are concentrated in tropical grasslands and savannahs. The results have important implications for the conservation of biodiversity in future, and for the ability of biodiversity to support important ecosystem functions, upon which humans rely.

Ce Zhang, Isabel Sargent, Xin Pan et al.

Urban land use information is essential for a variety of urban-related applications such as urban planning and regional administration. The extraction of urban land use from very fine spatial resolution (VFSR) remotely sensed imagery has, therefore, drawn much attention in the remote sensing community. Nevertheless, classifying urban land use from VFSR images remains a challenging task, due to the extreme difficulties in differentiating complex spatial patterns to derive high-level semantic labels. Deep convolutional neural networks (CNNs) offer great potential to extract high-level spatial features, thanks to its hierarchical nature with multiple levels of abstraction. However, blurred object boundaries and geometric distortion, as well as huge computational redundancy, severely restrict the potential application of CNN for the classification of urban land use. In this paper, a novel object-based convolutional neural network (OCNN) is proposed for urban land use classification using VFSR images. Rather than pixel-wise convolutional processes, the OCNN relies on segmented objects as its functional units, and CNN networks are used to analyse and label objects such as to partition within-object and between-object variation. Two CNN networks with different model structures and window sizes are developed to predict linearly shaped objects (e.g. Highway, Canal) and general (other non-linearly shaped) objects. Then a rule-based decision fusion is performed to integrate the class-specific classification results. The effectiveness of the proposed OCNN method was tested on aerial photography of two large urban scenes in Southampton and Manchester in Great Britain. The OCNN combined with large and small window sizes achieved excellent classification accuracy and computational efficiency, consistently outperforming its sub-modules, as well as other benchmark comparators, including the pixel-wise CNN, contextual-based MRF and object-based OBIA-SVM methods. The proposed method provides the first object-based CNN framework to effectively and efficiently address the complicated problem of urban land use classification from VFSR images.

Yao Yao, Xia Li, Xiaoping Liu et al.

Jing Wang, Yifan Lin, A. Glendinning et al.

Ensuring food security and sustainable development in China has been threatened by the dilemma of the rapidly growing consumption of the country’s land resources. Research on the linkage between land-use changes and land policies in the process of industrialization and urbanization has received increased attention in recent years. The present study was conducted to analyze the undergoing dynamics for Chinese land policies and land-use changes based on reliable land-use data and to develop a thorough understanding of the historical drivers and pathways of land-use changes and China’s deep-seated land issues, as well as the social, political and economic factors involved. The results showed that land-use changes were linked closely to shifts in government land policies and socio-economic development in China. The evolution of land policies in China was the result of a path-dependent process, which included the reform of land use system, the economic development environment as well as a policy-making process that responded to short-term land development. The results also indicated that there have been considerable achievements regarding the land use system and land management in China. However, Chinese economic growth overly depended on investments as well as land finance, which were uncoordinated and unsustainable. The changes in land use were also the outcomes of the land policy failure. There is still a pressing need to reform land policies for more efficient and effective utilization of limited land resources; develop a trade-off and synergy among urban development, agricultural production and ecosystem preservation; differentiate land-use policies; allocate market-oriented land resource; and establish a national macro-control mechanism in collaboration with a coordinated land-use policy and basic legislation.

Rahel Hamad, H. Balzter, K. Kolo

Multi-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.

A. Arowolo, Xiangzheng Deng, O. Olatunji et al.

Increasing human activities worldwide have significantly altered the natural ecosystems and consequently, the services they provide. This is no exception in Nigeria, where land-use/land-cover has undergone a series of dramatic changes over the years mainly due to the ever-growing large population. However, estimating the impact of such changes on a wide range of ecosystem services is seldom attempted. Thus, on the basis of GlobeLand30 land-cover maps for 2000 and 2010 and using the value transfer methodology, we evaluated changes in the value of ecosystem services in response to land-use/land-cover dynamics in Nigeria. The results showed that over the 10-year period, cultivated land sprawl over the forests and savannahs was predominant, and occurred mainly in the northern region of the country. During this period, we calculated an increase in the total ecosystem services value (ESV) in Nigeria from 665.93 billion (2007 US$) in 2000 to 667.44 billion (2007 US$) in 2010, 97.38% of which was contributed by cultivated land. The value of provisioning services increased while regulation, support, recreation and culture services decreased, amongst which, water regulation (-11.01%), gas regulation (-7.13%), cultural (-4.84%) and climate regulation (-4.3%) ecosystem functions are estimated as the most impacted. The increase in the total ESV in Nigeria associated with the huge increase in ecosystem services due to cultivated land expansion may make land-use changes (i.e. the ever-increasing agricultural expansion in Nigeria) appear economically profitable. However, continuous loss of services such as climate and water regulation that are largely provided by the natural ecosystems can result in huge economic losses that may exceed the apparent gains from cultivated land development. Therefore, we advocate that the conservation of the natural ecosystem should be a priority in future land-use management in Nigeria, a country highly vulnerable to climate change and incessantly impacted by natural disasters such as flooding.

A. Harper, Tom Powell, P. Cox et al.

Scenarios that limit global warming to below 2 °C by 2100 assume significant land-use change to support large-scale carbon dioxide (CO2) removal from the atmosphere by afforestation/reforestation, avoided deforestation, and Biomass Energy with Carbon Capture and Storage (BECCS). The more ambitious mitigation scenarios require even greater land area for mitigation and/or earlier adoption of CO2 removal strategies. Here we show that additional land-use change to meet a 1.5 °C climate change target could result in net losses of carbon from the land. The effectiveness of BECCS strongly depends on several assumptions related to the choice of biomass, the fate of initial above ground biomass, and the fossil-fuel emissions offset in the energy system. Depending on these factors, carbon removed from the atmosphere through BECCS could easily be offset by losses due to land-use change. If BECCS involves replacing high-carbon content ecosystems with crops, then forest-based mitigation could be more efficient for atmospheric CO2 removal than BECCS. Land-based mitigation for meeting the Paris climate target must consider the carbon cycle impacts of land-use change. Here the authors show that when bioenergy crops replace high carbon content ecosystems, forest-based mitigation could be more effective for CO2 removal than bioenergy crops with carbon capture and storage.

E. Blanc‐Betes, N. Gomez‐Casanovas, C. J. Bernacchi et al.

ABSTRACT The expansion of sugarcane onto land currently occupied by improved (IMP) and semi‐native (SN) pastures will reshape the U.S. bioenergy landscape. We combined biometric, ground‐based and eddy covariance methods to investigate the impact of sugarcane expansion across subtropical Florida on the carbon (C) budget over a 3‐year rotation. With 2.3‐ and 5.1‐fold increase in productivity over IMP and SN pastures, sugarcane displayed a C use efficiency (CUE; i.e., fraction of gross C uptake allocated to plant growth) of 0.59, well above that of pastures (0.31–0.23). Sugarcane also had greater C allocation to aboveground productivity and hence, harvestable biomass relative to IMP and SN. Cane heterotrophic respiration over the 3‐year rotation (903 ± 335 gC m−2 year−1) was 1% and 14% higher than IMP and SN pastures, respectively. These soil C losses responded largely to disturbance over the first year after conversion (1510 ± 227 gC m−2 year−1) but declined in subsequent years to an average 599 ± 90 gC m−2 year−1—well below those of IMP (933 ± 140 gC m−2 year−1) and SN (759 ± 114 gC m−2 year−1) pastures—despite a significant 40%–61% increase in soil C inputs. Soil C inputs, however, shifted from root‐dominated in pastures to litter‐dominated in sugarcane, with only 5% C allocation to roots. Reduced decomposition rates in sugarcane were likely driven by changes in the recalcitrance and distribution rather than the size of the newly incorporated soil C pool. As a result, we observed a rapid shift in the net ecosystem C balance (NECB) of sugarcane from a large source immediately following conversion to approaching the net C losses of IMP pastures only 2 years after conversion. The environmental cost of converting pasture to sugarcane underscores the importance of implementing management practices to harness the soil C storage potential of sugarcane in advancing a sustainable bioeconomy in Southeastern United States.

Perera, K.H, Kumara, A.S., Munasinghe, M.A.T.K.

The purpose of this paper is to explore the multi-dimensional determinants of voluntary tax compliance on the head of individual taxpayers. It critically appraises major theoretical frameworks and offers an extensive conceptual framework. The study is based on the qualitative methodology and performs the content analysis to synthesize information and form the conceptual model. VOSviewer software was used to conduct bibliometric analysis, spreadsheets were employed to study the data obtained and results were presented in terms of tables, graphs and figures to become more precise. The results also suggest that economic incentives, enforcement measures, human values, social obligation, and the perceptions of justice are a significant factor to influence voluntary compliance, and such personal values and ethical considerations become the key determinants, which supplement enforcement. The study advances the existing literature by incorporating personal values in the discussion of the topic of tax compliance in the context of developing countries, providing new perspectives to the policymakers aimed at developing a tax culture based on voluntary compliance with the help of clear, equitable and culturally competent policies.

Jason Hawkins, Mehrnoosh Zare

Anthropogenic greenhouse gas (GHG) emissions exhibit spatial variation owing to differences in development patterns, local climate, economic composition, energy sources, and other factors. Many of these factors - and therefore their contribution to GHG production - are influenceable through spatial planning and economic policy. Recent advances in environmental data reporting and climate flux measurement have produced high fidelity GHG emissions estimates at detailed spatial and temporal resolutions. Using one such dataset (Vulcan v3.0 1-km gridcell estimates of fossil-fuel CO2 for the U.S.), we explore the relationship between land use features and CO2 emissions. Analysis is conducted at multiple scales (neighbourhoods and metropolitan areas) to explore scale law effects. Using a data-driven propensity score approach, we develop doubly robust causal estimands for the effects of multiple land use features on CO2 emissions by sector. Preliminary results suggest that per capita transportation emissions are not significantly affected by local population density after controlling for metropolitan population density factors. However, results are likely influenced by the way transportation emissions are allocated in the Vulcan dataset. Results for scope 2 residential electricity and non-electricity energy are also considered in the study.

Babak Ghassemi, Cassio Fraga-Dantas, Raffaele Gaetano et al.

Land use and land cover mapping from Earth Observation (EO) data is a critical tool for sustainable land and resource management. While advanced machine learning and deep learning algorithms excel at analyzing EO imagery data, they often overlook crucial geospatial metadata information that could enhance scalability and accuracy across regional, continental, and global scales. To address this limitation, we propose BRIDGE-LC (Bi-level Representation Integration for Disentangled GEospatial Land Cover), a novel deep learning framework that integrates multi-scale geospatial information into the land cover classification process. By simultaneously leveraging fine-grained (latitude/longitude) and coarse-grained (biogeographical region) spatial information, our lightweight multi-layer perceptron architecture learns from both during training but only requires fine-grained information for inference, allowing it to disentangle region-specific from region-agnostic land cover features while maintaining computational efficiency. To assess the quality of our framework, we use an open-access in-situ dataset and adopt several competing classification approaches commonly considered for large-scale land cover mapping. We evaluated all approaches through two scenarios: an extrapolation scenario in which training data encompasses samples from all biogeographical regions, and a leave-one-region-out scenario where one region is excluded from training. We also explore the spatial representation learned by our model, highlighting a connection between its internal manifold and the geographical information used during training. Our results demonstrate that integrating geospatial information improves land cover mapping performance, with the most substantial gains achieved by jointly leveraging both fine- and coarse-grained spatial information.

Tomohiro Hirano, Alexis Akira Toda

Since McCallum (1987), it is well known that in an overlapping generations (OLG) economy with land, the equilibrium is Pareto efficient because with balanced growth, the interest rate exceeds the economic growth rate ($R>G$), which rules out infinite debt rollover (a Ponzi scheme). We show that once we remove knife-edge restrictions on the production function and allow unbalanced growth, under some conditions an efficient equilibrium with land bubbles necessarily emerges and infinite debt rollover becomes possible, which is a markedly different insight from the conventional view derived from the Diamond (1965) landless economy. We also examine the possibility of Pareto inefficient equilibria.

Yusuke Adachi

This paper develops a novel method for estimating the housing production function that addresses transmission bias caused by unobserved heterogeneity in land productivity. The approach builds on the nonparametric identification strategy of Gandhi et al. (2020) and exploits the zero-profit condition to allow consistent estimation even when either capital input or housing value is unobserved, under the assumption that land productivity follows a Markov process. Monte Carlo simulations demonstrate that the estimator performs well across a variety of production technologies.

S. Georganos, T. Grippa, S. Vanhuysse et al.