Modeling the impact of large-scale transportation infrastructure development on land cover

2016 ◽  
Vol 10 (1) ◽  
pp. 26-42 ◽  
Author(s):  
Dimitrios Efthymiou ◽  
Constantinos Antoniou ◽  
Emmanouela Siora ◽  
Demetre Argialas
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Transportation Infrastructure ◽  
Infrastructure Development ◽  
The Impact

CAN INFRASTRUCTURE DEVELOPMENT ALLEVIATE MULTIDIMENSIONAL POVERTY? — EVIDENCE FROM CHINA

2021 ◽  
pp. 1-34
Author(s):  
YONGBO GE ◽  
YUEXIAO ZHU ◽  
WENQIANG ZHANG ◽  
XIAORAN KONG
Keyword(s):  
Poverty Alleviation ◽  
Capital Accumulation ◽  
High Speed ◽  
Large Scale ◽  
Multidimensional Poverty ◽  
Infrastructure Development ◽  
Public Infrastructure ◽  
Regional Heterogeneity ◽  
The Impact ◽  
Insight Into

We investigate the impact of the construction of large-scale high-speed railways (HSRs) on regional multidimensional poverty in China. We find that the opening of HSRs can reduce this poverty indicator. This association is robust to a series of checks. Regarding the mechanisms, the opening of HSRs can improve regional accessibility, enhance local tourism, increase labor mobility and promote human capital accumulation, which alleviates multidimensional poverty. Further research indicates the regional heterogeneity of the effect. This research supplements poverty alleviation theory from the perspective of public infrastructure and offers insight into how multidimensional poverty arises and how it can be alleviated.

Does China’s transportation infrastructure have an impact on employment in the service sector?

Kybernetes ◽  
2020 ◽  
Vol 49 (11) ◽  
pp. 2737-2753
Author(s):  
Hui Wang ◽  
Meiqing Zhang
Keyword(s):  
Large Scale ◽  
Service Industry ◽  
Spillover Effect ◽  
Transportation Infrastructure ◽  
Content Type ◽  
Inland Waterways ◽  
Spatial Spillover ◽  
Positive Effects ◽  
The Impact ◽  
Spatial Spillover Effect

Purpose The large-scale construction of China’s transportation infrastructure has driven the flow of elements between regions, which has provided convenient conditions for the accumulation of advantageous resources. Design/methodology/approach Based on the panel data of 31 provinces in China in the past 2003-2017 years, this paper applies the spatial econometric model and partial differential method and empirically analyzes the spatial spillover effect of transportation infrastructure on employment in the service industry under four spatial weighting matrices. Findings The results show that for every 1 per cent increase in the level of transportation infrastructure, the employment density of the service industry in the region can be increased by 0.1274 per cent. It is worth noting that roads promote the employment of the service industry more than railways and inland waterways. However, inland waterways have not shown positive effects. The results on spatial spillover of transportation infrastructure indicate that railway has obvious promotion effect on the employment level of service industry in the surrounding area, while the highway has hindered the effect. The spatial spillover effect of inland waterway is not obvious. Originality/value The value of this paper is to consider the impact of China’s transportation infrastructure on employment in a particular industry, especially in the service industry. The research will help to provide empirical evidence for policymakers. The government needs to invest and build transportation infrastructure based on the stage and development potential of the employment development of the regional service industry.

scholarly journals V2Karst V1.0: A parsimonious large-scale integrated vegetation-recharge model to simulate the impact of climate and land cover change in karst regions

2018 ◽  
Author(s):  
Fanny Sarrazin ◽  
Andreas Hartmann ◽  
Francesca Pianosi ◽  
Thorsten Wagener
Keyword(s):  
Land Cover ◽  
Groundwater Recharge ◽  
Land Cover Change ◽  
Large Scale ◽  
Synthetic Data ◽  
Temporal Distribution ◽  
Soil Parameters ◽  
Large Scale Assessment ◽  
Karst Areas ◽  
The Impact

Abstract. Karst aquifers are an important source of drinking water in many regions of the world. Karst areas are highly permeable and produce large amounts of groundwater recharge, while surface runoff is typically negligible. As a result, recharge in these systems may have a different sensitivity to climate and land cover changes compared to other less permeable systems. However, little effort has been directed toward assessing the impact of climate and land cover change in karst areas at large-scales. In this study, we address this gap by (1) introducing the first large-scale hydrological model including an explicit representation of both karst and land cover properties, and by (2) analysing the model's recharge production behaviour. To achieve these points, we first improve the evapotranspiration estimation of a previous large-scale karst recharge model (VarKarst). The new model (V2Karst V1.0) includes a parsimonious representation of relevant ET processes for climate and land cover change impact studies. We demonstrate the plausibility of V2Karst simulations at carbonate rock FLUXNET sites using soft rules and global sensitivity analysis. Then, we use virtual experiments with synthetic data to assess the sensitivity of simulated recharge to precipitation characteristics and land cover. Results reveal how both vegetation and soil parameters control the model behaviour, and they suggest that simulated recharge is sensitive to both precipitation (overall amount and temporal distribution) and land cover. Large-scale assessment of future karst groundwater recharge should therefore consider the combined impact of changes in land cover and precipitation properties, if it is to produce realistic projections of future change impacts.

scholarly journals Detecting the impact of land cover change on observed rainfall

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7523 ◽  
Author(s):  
Chun Xia Liang ◽  
Floris F. van Ogtrop ◽  
R. Willem Vervoort
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Large Scale ◽  
Southern Oscillation ◽  
Rainfall Variability ◽  
Tree Cover ◽  
Trend Test ◽  
South Central ◽  
The Impact ◽  
Step Trend

Analysis of observational data to pinpoint impact of land cover change on local rainfall is difficult due to multiple environmental factors that cannot be strictly controlled. In this study we use a statistical approach to identify the relationship between removal of tree cover and rainfall with data from best available sources for two large areas in Australia. Gridded rainfall data between 1979 and 2015 was used for the areas, while large scale (exogenous) effects were represented by mean rainfall across a much larger area and climatic indicators, such as Southern Oscillation Index and Indian Ocean Dipole. Both generalised additive modelling and step trend tests were used for the analysis. For a region in south central Queensland, the reported change in tree clearing between 2002–2005 did not result in strong statistically significant precipitation changes. On the other hand, results from a bushfire affected region on the border of New South Wales and Victoria suggest significant changes in the rainfall due to changes in tree cover. This indicates the method works better when an abrupt change in the data can be clearly identified. The results from the step trend test also mainly identified a positive relationship between the tree cover and the rainfall at p < 0.1 at the NSW/Victoria region. High rainfall variability and possible regrowth could have impacted the results in the Queensland region.

Impact of Abrupt Land Cover Changes by Tropical Deforestation on Southeast Asian Climate and Agriculture

2017 ◽  
Vol 30 (7) ◽  
pp. 2587-2600 ◽  
Author(s):  
Merja H. Tölle ◽  
Steven Engler ◽  
Hans-Jürgen Panitz
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Layer Structure ◽  
Surface Fluxes ◽  
Maximum Temperature ◽  
Natural Mode ◽  
Land Cover Changes ◽  
Strong Impact ◽  
Se Asia ◽  
The Impact

Southeast Asia (SE Asia) undergoes major and rapid land cover changes as a result of agricultural expansion. Landscape conversion results in alterations to surface fluxes of moisture, heat, and momentum and sequentially impact the boundary layer structure, cloud-cover regime, and all other aspects of local and regional weather and climate occurring also in regimes remote from the original landscape disturbance. The extent and magnitude of the anthropogenic modification effect is still uncertain. This study investigates the biogeophysical effects of large-scale deforestation on monsoon regions using an idealized deforestation simulation. The simulations are performed using the regional climate model COSMO-CLM forced with ERA-Interim data during the period 1984–2004. In the deforestation experiment, grasses in SE Asia, between 20°S and 20°N, replace areas covered by trees. Using principal component analysis, it is found that abrupt conversion from forest to grassland cover leads to major climate variability in the year of disturbance, which is 1990, over SE Asia. The persistent land modification leads to a decline in evapotranspiration and precipitation and a significant warming due to reduced latent heat flux during 1990–2004. The strongest effects are seen in the lowlands of SE Asia. Daily precipitation extremes increase during the monsoon period and ENSO, differing from the result of mean precipitation changes. Maximum temperature also increases by 2°C. The impacts of land cover change are more intense than the effects of El Niño and La Niña. In addition, results show that these land clearings can amplify the impact of the natural mode ENSO, which has a strong impact on climate conditions in SE Asia. This will likely have consequences for the agricultural output.

Impact of Major Road Supply on Individual Travel Time Expenditure: An Exploration with a 30-Year Variation of Infrastructure and Travel

2018 ◽  
Vol 2672 (3) ◽  
pp. 56-68
Author(s):  
Ryosuke Abe ◽  
Kay W. Axhausen
Keyword(s):  
Travel Time ◽  
Large Scale ◽  
Travel Demand ◽  
Road Traffic ◽  
Land Development ◽  
Transportation Infrastructure ◽  
Major Road ◽  
Birth Cohorts ◽  
Data Set ◽  
The Impact

This study estimates the impact of major road supply on individual travel time expenditures (TTEs) using data that cover 30-year variations in transportation infrastructure and travel behavior. The impacts of the supply of road and rail infrastructure are estimated with a data set that combines records of large-scale household travel surveys in the Tokyo metropolitan area conducted in 1978, 1988, 1998, and 2008. Linear and Tobit models of individual TTEs are estimated by following the behavior of birth cohorts over the 30-year period. The models incorporate the changes in transportation infrastructure, measured as lane kilometers of two levels of major road stock and vehicle kilometers of urban rail service. The results show significant negative effects of lane kilometers for higher-level and lower-level major roads on the TTEs for all travel purposes and for commuting, after controlling for socioeconomic backgrounds and generations of individuals. This study discusses that, in Tokyo, the estimated effect is more likely to reflect the effect of a major road network per se on individual TTEs than the (indirect) effect of major road supply on individual TTEs working through land development activities (i.e., induced car travel demand). For example, the caveat is that actual road investment decisions still need to consider the induced component of road traffic in addition to the (direct) effect that is estimated in this study.

scholarly journals V2Karst V1.1: a parsimonious large-scale integrated vegetation–recharge model to simulate the impact of climate and land cover change in karst regions

2018 ◽  
Vol 11 (12) ◽  
pp. 4933-4964 ◽  
Author(s):  
Fanny Sarrazin ◽  
Andreas Hartmann ◽  
Francesca Pianosi ◽  
Rafael Rosolem ◽  
Thorsten Wagener
Keyword(s):  
Land Cover ◽  
Groundwater Recharge ◽  
Large Scale ◽  
Temporal Distribution ◽  
Land Cover Changes ◽  
Depth Analysis ◽  
Karst Areas ◽  
Large Scale Land ◽  
Evapotranspiration Estimation ◽  
The Impact

Abstract. Karst aquifers are an important source of drinking water in many regions of the world. Karst areas are highly permeable and produce large amounts of groundwater recharge, while surface runoff is often negligible. As a result, recharge in these systems may have a different sensitivity to climate and land cover changes than in other less permeable systems. However, little is known about the combined impact of climate and land cover changes in karst areas at large scales. In particular, the representation of land cover, and its controls on evapotranspiration, has been very limited in previous karst hydrological models. In this study, we address this gap (1) by introducing the first large-scale hydrological model including an explicit representation of both karst and land cover properties, and (2) by providing an in-depth analysis of the model's recharge production behaviour. To achieve these aims, we replace the empirical approach to evapotranspiration estimation of a previous large-scale karst recharge model (VarKarst) with an explicit, mechanistic and parsimonious approach in the new model (V2Karst V1.1). We demonstrate the plausibility of V2Karst simulations at four carbonate rock FLUXNET sites by assessing the model's ability to reproduce observed evapotranspiration and soil moisture patterns and by showing that the controlling modelled processes are in line with expectations. Additional virtual experiments with synthetic input data systematically explore the sensitivities of recharge to precipitation characteristics (overall amount and temporal distribution) and land cover properties. This approach confirms that these sensitivities agree with expectations and provides first insights into the potential impacts of future change. V2Karst is the first model that enables the study of the joint impacts of large-scale land cover and climate changes on groundwater recharge in karst regions.

A study of decadal Land use-land cover change in Dehradun city.

2021 ◽  
Author(s):  
Gayatri Singh
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Water Levels ◽  
Physical Factors ◽  
Infrastructure Development ◽  
Land Use Land Cover ◽  
Detection Analysis ◽  
The Impact

&lt;p&gt;The present study is to quantify the spatial-temporal pattern of the Land Use/ Land Cover Change (LULCC) during a decade (i.e., 2010 to 2020) in the Dehradun city which is situated in the foothills of the Himalaya, using Landsat data. The study helps in identifying the major bio-physical factors governing LULCC through modern geospatial techniques. Change detection shows that the study area experienced an increase in its urban area from 2010 to 2020 and a comparatively decrease in cropland and forest area. This was due to an increase in its urban population, rapid increase in industrialization and tourism during the same period. The change detection analysis further shows that 2010-2020, associated with change in croplands, change in built-up, forest lands, change in water-bodies, water levels, and rainfall. With comparison of above results and collected socio-economic data in this region, the impact of changing land use &amp; bio-physical/ economic factors on agricultural profitability were analyzed. The result of this study could thus lead to a detailed and lucid spatiotemporal assessment of the major bio-physical factors. It is expected that the study will help in facilitating better policy making and infrastructure development for industries and urbanization.&lt;br&gt;&lt;br&gt;&lt;/p&gt;

The impact of large scale agriculture on forest and wildlife in Diga Woreda, Western Ethiopia

2017 ◽  
Vol 1 (02) ◽  
pp. 100-113
Author(s):  
OLANI GANFURE JALETA ◽  
HABTE JEBESSA DEBELLA
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Forest Cover ◽  
Satellite Image ◽  
Forest Cover Change ◽  
State Farm ◽  
Degraded Lands ◽  
Local Study ◽  
The Impact ◽  
Western Ethiopia

Jaleta OG, Jebessa H. 2018. The impact of large scale agriculture on forest and wildlife in Diga Woreda, Western Ethiopia. Asian J Agric 1: 100-113. Large-scale agriculture uses agricultural machinery to mechanize the practices of agriculture. It is one of the leading causes of the loss of forest and wildlife in many countries including our country, Ethiopia. Information on forest cover change that occurred from 1986 to 2006 in Diga Woreda/district (Woyessa Dimtu, Bekiltu Gudina, and Melka Beti Jirma Kebeles) was compared with the present time using Geographic Information System (GIS). The objective of this study was to investigate the impact of large-scale agriculture on forest cover change by using the satellite image of the study area and other data collecting methods such as household's interview, KI, FGD and observation (survey) to detect its effect on wildlife. The study employed both qualitative and quantitative data as well as primary and secondary data sources to collect necessary information. The information providers were purposively selected from sample ‘kebeles' based on their age and experiences, that is, to get a detail and accurate information elders and experts who have lived in the area for many years and who know more how and when the Hanger-Didessa state farm had established were selected. The state farm covered a large area, that is, about four districts such as Sasiga, Diga, Arjo and Guto Gida. For this study, Diga was selected because of its socio-economic characteristics, deforested (degraded) area, local loss of larger mammals and forest cover changes observed in the district. The descriptive research method was used to assess community's knowledge, perception, skill, and feeling about the impact of Local Study Area (LSA) on forest and wildlife in the area. Land cover change analysis for 1986 to 2006 showed that the land cover of the study area is classified as grazing, wood, agricultural, settlement and degraded lands. The result of the analysis showed that agriculture, settlement and degraded lands increased from 19.68% to 32.72%, 12.12% to 26.85% and 2.76% to 4.72% respectively in an expense of a decrease in the grass (grazing) and woodlands. Therefore, LSA is the primary cause for the loss of forest and wildlife in the study area.

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