scholarly journals Assessment of Human-Related Driving Forces for Reduced Carbon Uptake Using Neighborhood Analysis and Geographically Weighted Regression: A Case Study in the Grassland of Inner Mongolia, China

2020 ◽  
Vol 10 (21) ◽  
pp. 7787
Author(s):  
Zongyao Sha ◽  
Ruren Li
Keyword(s):  
Carbon Sequestration ◽  
Geographically Weighted Regression ◽  
Inner Mongolia ◽  
Road Network ◽  
Driving Forces ◽  
Grazing Intensity ◽  
Grassland Management ◽  
Weighted Regression ◽  
Carbon Uptake ◽  
Neighborhood Analysis

The ever-rising concentration of atmospheric carbon is viewed as the primary cause for global warming. To discontinue this trend, it is of urgent importance to either cut down human carbon emissions or remove more carbon from the atmosphere. Grassland ecosystems occupy the largest part of the global land area but maintain a relatively low carbon sequestration flux. While numerous studies have confirmed the impacts on grassland vegetation growth from climate changes and human activities, little work has been done to understand the driving forces for a reduced carbon uptake (RCU)—a loss in vegetation carbon sequestration because of inappropriate grassland management. This work focused on assessing RCU in the grassland of Inner Mongolia and understanding the influential patterns of the selected variables (including grazing intensity, road network, population, and vegetation productivity) related to RCU. Neighborhood analysis was proposed to locate optimized grassland management practices from historical data and to map RCU. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were applied to explore the driving forces for RCU. The results indicated that the human-related factors, including stock grazing intensity, population density, and road network were likely to present a spatially varied impact on RCU, which accounted for more than 1/4 of the total carbon sequestration.

scholarly journals Exploring the Dynamics of Urban Greenness Space and Their Driving Factors Using Geographically Weighted Regression: A Case Study in Wuhan Metropolis, China

Land ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 500
Author(s):  
Chengjie Yang ◽  
Ruren Li ◽  
Zongyao Sha
Keyword(s):  
Urban Planning ◽  
Geographically Weighted Regression ◽  
Driving Forces ◽  
Vital Role ◽  
Google Earth ◽  
Weighted Regression ◽  
Landscape Indices ◽  
Aggregation Index ◽  
Terrain Elevation

Urban greenness plays a vital role in supporting the ecosystem services of a city. Exploring the dynamics of urban greenness space and their driving forces can provide valuable information for making solid urban planning policies. This study aims to investigate the dynamics of urban greenness space patterns through landscape indices and to apply geographically weighted regression (GWR) to map the spatially varied impact on the indices from economic and environmental factors. Two typical landscape indices, i.e., percentage of landscape (PLAND) and aggregation index (AI), which measure the abundance and fragmentation of urban greenness coverage, respectively, were taken to map the changes in urban greenness. As a case study, the metropolis of Wuhan, China was selected, where time-series of urban greenness space were extracted at an annual step from the Landsat collections from Google Earth Engine during 2000–2018. The study shows that the urban greenness space not only decreased significantly, but also tended to be more fragmented over the years. Road network density, normalized difference built-up index (NDBI), terrain elevation and slope, and precipitation were found to significantly correlate to the landscape indices. GWR modeling successfully captures the spatially varied impact from the considered factors and the results from GWR modeling provide a critical reference for making location-specific urban planning.

scholarly journals Multi-year CO<sub>2</sub> budgets in South African semi-arid Karoo ecosystems under different grazing intensities

2020 ◽  
Author(s):  
Oksana Rybchak ◽  
Justin du Toit ◽  
Jean-Pierre Delorme ◽  
Jens-Kristian Jüdt ◽  
Kanisios Mukwashi ◽  
...  
Keyword(s):  
Carbon Sequestration ◽  
Carbon Source ◽  
South African ◽  
Water Availability ◽  
Carbon Sink ◽  
Grazing Intensity ◽  
Livestock Grazing ◽  
Carbon Uptake ◽  
The Past ◽  
Semi Arid

Abstract. Climatic and land management factors, such as water availability and grazing intensity, play an important role in seasonal and annual variability of the ecosystem–atmosphere exchange of CO2 in semi-arid ecosystems. However, the semi-arid South African ecosystems have been poorly studied. Four years of measurements (November 2015–October 2019) were collected and analysed from two eddy covariance towers near Middelburg in the Karoo, Eastern Cape, South Africa. We studied the impact of grazing intensity on the CO2 exchange by comparing seasonal and interannual CO2 fluxes for two sites with almost identical climatic conditions but different intensity of current and historical livestock grazing. The first site represents lenient grazing (LG) and the vegetation comprises a diverse balance of dwarf shrubs and grasses, while the second site has been degraded through heavy grazing (HG) in the past but then rested for the past 10 years and mainly consists of unpalatable grasses and ephemeral species. Over the observation period, we found that the LG site was a considerable carbon source (82.11 g C m−2), while the HG site was a slight carbon sink (−36.43 g C m−2). The annual carbon budgets ranged from −90 ± 51 g C m−2 yr−1 to 84 ± 43 g C m−2 yr−1 for the LG site and from −92 ± 66 g C m−2 yr−1 to 59 ± 46 g C m−2 yr−1 for the heavily grazed site over the four years of eddy covariance measurements. The significant variation in carbon sequestration rates between the last two years of measurement was explained by water availability (25 % of the precipitation deficit in 2019 compared to the long-term mean precipitation). This indicates that studied ecosystems can quickly switch from a considerable carbon sink to a considerable carbon source ecosystem. Our study shows that the CO2 dynamics in the Karoo are largely driven by water availability and the current and historical effects of livestock grazing intensity on aboveground biomass (AGB). The higher carbon uptake at the HG site indicates that resting period after overgrazing, together with the transition to unpalatable drought-tolerant grass species, creates conditions that are favourable for carbon sequestration in the Karoo ecosystems, but unproductive as Dorper sheep pasture. Furthermore, we observed a slight decrease in carbon uptake peaks at the HG site in response to resuming continuous grazing (July 2017).

scholarly journals Market Costs and Financing Options for Grassland Carbon Sequestration: Empirical and Modelling Evidence From Qinghai, China

2021 ◽  
Vol 9 ◽  
Author(s):  
Andreas Wilkes ◽  
Shiping Wang ◽  
Leslie Lipper ◽  
Xiaofeng Chang
Keyword(s):  
Climate Change ◽  
Carbon Sequestration ◽  
Grazing Intensity ◽  
Grassland Management ◽  
Cash Flows ◽  
Economic Benefits ◽  
The Tibetan Plateau ◽  
Degraded Land ◽  
Financing Costs ◽  
Carbon Project

Asia’s grasslands provide livelihoods for some of the region’s poorest people. Widespread grassland degradation reduces the resilience and returns to herding livelihoods. Reversing degradation and conserving grasslands could not only improve herders’ situation, but also make a huge contribution to mitigating climate change by sequestering carbon in soils. However, the means for reaching each of these objectives are not necessarily the same. To realize this potentially huge dual livelihood/climate change mitigation outcome from improved grassland management, it is necessary to have detailed understanding of the processes involved in securing better livelihoods and sequestering carbon. Based on household surveys on the Tibetan Plateau and modeling results, this study estimates economic and market costs of grassland carbon sequestration, and analyzes the implications of household and carbon project cash flows for the design of financing options. Five scenarios are modeled involving cultivation of grass on severely degraded grassland (all scenarios) and reduced grazing intensity on less degraded land, which requires destocking by 29, 38, 47, 56, and 65% in each scenario). Modeling results suggest that economic benefits for herders are positive at low levels of destocking, and negative at high levels of destocking, but initial investments and opportunity costs are significant barriers to adoption for households in all destocking scenarios. Existing rural finance products are not suitable for herders to finance the necessary investments. Market costs–the cost at which transactions between herders and carbon project developers are feasible–depend on the scale of project implementation but are high compared to recent carbon market prices. Large initial investments increase project developers’ financing costs and risk, so co-financing of initial investments by government would be necessary. Therefore, public policies to support grassland carbon sequestration should consider the potential roles of a range of financial instruments.

Exploring the driving forces behind deforestation in the state of Mexico (Mexico) using geographically weighted regression

2010 ◽  
Vol 30 (4) ◽  
pp. 576-591 ◽  
Author(s):  
Noel Bonfilio Pineda Jaimes ◽  
Joaquín Bosque Sendra ◽  
Montserrat Gómez Delgado ◽  
Roberto Franco Plata
Keyword(s):  
Geographically Weighted Regression ◽  
Driving Forces ◽  
The State ◽  
Weighted Regression

Study on Driving Factors of Carbon Emissions in Inner Mongolia of China Based on Geographically Weighted Regression Model

2014 ◽  
Vol 962-965 ◽  
pp. 2355-2359
Author(s):  
Ri Na Wu ◽  
Ming Xiang Huang ◽  
Yu Hai Bao ◽  
Gang Bao
Keyword(s):  
Spatial Heterogeneity ◽  
Carbon Emissions ◽  
Geographically Weighted Regression ◽  
Inner Mongolia ◽  
Industrial Structure ◽  
Social Economic ◽  
Driving Factors ◽  
Weighted Regression ◽  
Road Density ◽  
Geographically Weighted Regression Model

In this paper, based on the data of carbon emissions of county-level in Inner Mongolia autonomous region of China, using the Geographically Weighted Regression (GWR) model, we quantitatively analyze the effects of six social-economic driving factors, including Gross Domestic Product (GDP), population (Popu), economic growth rate (EconGR), urbanization (Urba), industrial structure (InduS) and road density (RoadD) on regional carbon emissions. The results were achieved as follow:(1) The spatial heterogeneity of carbon emissions of Inner Mongolia and the social-economic factors of affecting carbon emissions are obviously; (2) the correlation among the six factors is low. (3) GDP, InduS and Popu have significant effect on carbon emissions, and effects of EconGR, Urba and RoadD are smaller. The impacts of different factors on carbon emissions at different spatial region show spatial heterogeneity.

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