Carbon emissions induced by land-use and land-cover change from 1970 to 2010 in Zhejiang, China

Zhu, Enyan; Deng, Jianan; Zhou, Mengmeng; Gan, Muye; Jiang, Ruowei; Wang, Ke; Shahtahmassebi, AmirReza

doi:10.1016/j.scitotenv.2018.07.317

Cited by 169 publications

(96 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Historical changes in SC stocks due to land management practices such as deforestation and agricultural tillage have been responsible for a significant fraction of historical anthropogenic carbon dioxide (CO 2 ) emissions to the atmosphere (Eglin et al, 2010;Houghton et al, 2012). Conventionally tilled soils can lose from 20 to 50% of SC relative to unmanaged or no-till practices (Mann, 1985;Senthilkumar et al, 2009;Bruun et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Land Use and Land Cover Affect the Depth Distribution of Soil Carbon: Insights From a Large Database of Soil Profiles

Sulman

Harden

et al. 2020

Front. Environ. Sci.

View full text Add to dashboard Cite

Soils contain a large and dynamic fraction of global terrestrial carbon stocks. The distribution of soil carbon (SC) with depth varies among ecosystems and land uses and is an important factor in calculating SC stocks and their vulnerabilities. Systematic analysis of SC depth distributions across databases of SC profiles has been challenging due to the heterogeneity of soil profile measurements, which vary in depth sampling. Here, we fit over 40,000 SC depth profiles to an exponential decline relationship with depth to determine SC concentration at the top of the mineral soil, minimum SC concentration at depth, and the characteristic "length" of SC concentration decline with depth. Fitting these parameters allowed profile characteristics to be analyzed across a large and heterogeneous dataset. We then assessed the differences in these depth parameters across soil orders and land cover types and between soil profiles with or without a history of tillage, as represented by the presence of an Ap horizon. We found that historically tilled soils had more gradual decreases of SC with depth (greater e-folding depth or Z *), deeper SC profiles, lower SC concentrations at the top of the mineral soil, and lower total SC stocks integrated to 30 cm. The large database of profiles allowed these results to be confirmed across different land cover types and spatial areas within the Continental United States, providing robust evidence for systematic impacts of historical tillage on SC stocks and depth distributions.

show abstract

Section: Introductionmentioning

confidence: 99%

Land Use and Land Cover Affect the Depth Distribution of Soil Carbon: Insights From a Large Database of Soil Profiles

Sulman

Harden

et al. 2020

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Based on results from existing research [ 16 , 35 ], we formulate the total carbon emission as below:

where

is the total carbon emission,

is a land-use type,

represents the carbon emission from land-use type

is the area of land-use type

, and

represents the carbon emission coefficient for land-use type

. By referring to previous studies [ 36 , 46 ], the carbon emission coefficients are determined as follows: 0.421 ton/(hm 2 ·a) for cultivated land, −0.731 ton/(hm 2 ·a) for garden land, −0.614 ton/(hm 2 ·a) for woodland, −0.022 ton/(hm 2 ·a) for grassland, 47.792 ton/(hm 2 ·a) for transportation land, −0.253 ton/(hm 2 ·a) for water areas, 33.651 ton/(hm 2 ·a) for construction land, 0.032 ton/(hm 2 ·a) for unutilized land.…”

Section: Methodology and Data Sourcesmentioning

confidence: 99%

“…As the main sources of carbon emissions, production and residential land have negative effects on the regional eco-environment. On the other hand, ecological land, such as woodland, grassland, and water areas, can promote environmental purification through carbon absorption, which has a positive effect on the environment [46]. Therefore, we chose carbon emissions as the undesirable output.…”

Section: Variable Selection and Data Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Allocation Efficiency, Influencing Factors and Optimization Path of Rural Land Resources: A Case Study in Fang County of Hubei Province, China

Yang

Wang

Zhang

et al. 2020

IJERPH

View full text Add to dashboard Cite

Land resource allocation efficiency (LRAE) is a significant indicator in weighing regional socioeconomic development. The study of LRAE can provide useful references for optimizing the layout of rural land use and conducting village planning against the background of rural revitalization strategy. Taking Fang County of Hubei Province as an example, we constructed an efficiency measurement index system based on economic, social, and ecological objectives. The slack-based measure with undesirable output (SBM-Undesirable) model and geodetector model were used to evaluate the rural LRAE, influencing factors and optimization paths from 2011 to 2017. The results suggest that: (1) the rural LRAE in Fang County shows a steady upward trend, with an average increasing rate of 9.204%. The townships in the north and south of the study area have a low LRAE value, and townships in the central area have a high LRAE value. The number of villages at low or medium-low LRAE is decreasing, and the number of villages with medium-high or high LRAE continued to increase from 2011 to 2017. (2) The spatial variation in LRAE in Fang County is affected by physical geography conditions, rural development conditions, and urban-rural relations. The impact of the proportion of primary industry and rural population has always been influential on the LRAE. Physical geography conditions have a relatively strong impact on the LRAE, but their values are decreasing. The influences of the Engel coefficient, urbanization rate and gap between the rural and urban resident’s income have been continuously enhanced. (3) All land types have obvious input redundancies, and reducing these redundancies can help achieve the optimal allocation of rural land resources. In the future, it is of significance to prioritize low-carbon and green developments, and to promote sustainable rural development.

show abstract

“…Indeed, since the mid-1990s, the industrialization and urbanization have grown in Zhejiang Province, with population rising from 22.8 million in 1950 to 56.6 million in 2017. However, the farmland area of the province has declined by 20.49%, of 5900 km 2 , from 1990 to 2010 (Wu et al 2015;Zhu et al 2019). In addition, a large number of rural workers engaged in agricultural activities are now working in manufacturing and service sectors, leading to the reduction in agricultural manpower.…”

Section: Evolution Of Carbon Ecological Deficitmentioning

confidence: 99%

High spatio-temporal heterogeneity of carbon footprints in the Zhejiang Province, China, from 2005 to 2015: implications for climate change policies

et al. 2020

View full text Add to dashboard Cite

Energy consumption is a major factor influencing climate change. Low-carbon strategies can be defined from carbon (C) footprint and ecological pressure caused by energy consumption. We hypothesized that this should be done at the regional and city levels because factors controlling C footprints vary with location and time, yet there is actually little knowledge at local levels. Here, we mapped the C emissions and footprint of 11 cities in the Zhejiang Province of China, a rapidly growing area of about 57 million inhabitants, which is representative of the strong economic growth in China. The C footprint is expressed as the land surface necessary to adsorb C emissions. We studied spatio-temporal evolution of C footprints from 2005 to 2015 using the center of gravity method. Results show that total annual C emissions due to energy consumption in Zhejiang increased by 40.83 million tons (55.8%

show abstract

Carbon emissions induced by land-use and land-cover change from 1970 to 2010 in Zhejiang, China

Cited by 169 publications

References 21 publications

Land Use and Land Cover Affect the Depth Distribution of Soil Carbon: Insights From a Large Database of Soil Profiles

Land Use and Land Cover Affect the Depth Distribution of Soil Carbon: Insights From a Large Database of Soil Profiles

Allocation Efficiency, Influencing Factors and Optimization Path of Rural Land Resources: A Case Study in Fang County of Hubei Province, China

High spatio-temporal heterogeneity of carbon footprints in the Zhejiang Province, China, from 2005 to 2015: implications for climate change policies

Contact Info

Product

Resources

About