Spatial-Temporal Characteristics and Influencing Factors of Carbon Emissions from Land Use and Land Cover in Black Soil Region of Northeast China Based on LMDI Simulation

Abstract

Land use change accounts for a large proportion of the carbon emissions produced each year, especially in highly developed traditional heavy industry and agriculture areas. In this study, we estimated the carbon emissions from land use in the Black Soil Region of Northeast China (BSRNC) from 1990 to 2020. We utilized seven periods of land use remote sensing image data spanning the years 1990, 1995, 2000, 2005, 2010, 2015, and 2020, with a 30-m grid resolution. Additionally, socio-economic data was incorporated into the analysis. The preprocessing of the remote sensing images involved several steps using ENVI 5.5, including radiometric correction, fusion, mosaic, and cropping. The land types were classified into six major categories: cropland, forest land, grassland, water area, construction land, and unused land, using the LUCC classification system. The IPCC coefficient method was used to calculate the trends in carbon emissions from land use, and the logarithmic mean Divisia index (LMDI) method was applied to analyze the influencing factors. The main conclusions are as follows: (1) From 1990 to 2020, the net carbon emissions from land use in the BSRNC increased from 11.91 × 104 t to 253.29 × 104 t, with an annual growth rate of 8.04%. (2) Spatially, land use carbon emissions exhibited an agglomeration pattern that gradually weakened and the regional emission differences gradually narrowed. (3) Income level was identified as the most important factor influencing land use carbon emissions in the BSRNC from 1990 to 2020. Land use efficiency had a inhibitory effect on net carbon emissions, reducing land use carbon emissions by 1730.63 × 104 t.

Examining the Social Costs of Carbon Emissions and the Ecosystem Service Value in Island Ecosystems: An Analysis of the Zhoushan Archipelago

“…The appropriate literature was consulted to derive the carbon emission coefficients for different forms of land use. According to the principle of similar latitude and longitude and similar climatic conditions, the most extensive reference values were selected to determine the carbon emission coefficient of each land-use type, with values as follows: farmland (0.422) [52], forest (−0.581) [53], shrubland (−0.161) [54], grassland (−0.021) [55], waterbody (−0.253) [52], unused land (−0.005) [56], and mudflat (−1.538) [57].…”

Characteristics of Land-Use Carbon Emissions and Carbon Balance Zoning in the Economic Belt on the Northern Slope of Tianshan

“…Previous articles also revealed that the main sources of CEs from land use were mainly the activities of mankind on the land, as well as the maintenance of and changes in the land itself [11] Meanwhile, land is a field and vital effecting factor for CEs from energy consumption Thus, it was suggested that human activities on the land were the greatest factor for [12] Most of the human activities were carried by land, and the land-use structures of differen countries and areas are different to some extent, and accordingly, the mechanism of land use CE was complex and contains several uncertain factors [13]. Comprehensive studies on land-use CE and its characteristics were beneficial for improving the calculation accu racy of CEs, by which the affecting factors would be further clarified [14][15][16]. In addition the concrete analysis on specific issues could be implemented via focalized studies on sec tional land-use CEs, which is greatly conductive with the adjustment of land-use structure and further formulation of relevant policies on CE reduction [17].…”

“…Most of the human activities were carried by land, and the land-use structures of different countries and areas are different to some extent, and accordingly, the mechanism of land-use CE was complex and contains several uncertain factors [13]. Comprehensive studies on land-use CE and its characteristics were beneficial for improving the calculation accuracy of CEs, by which the affecting factors would be further clarified [14][15][16]. In addition, the concrete analysis on specific issues could be implemented via focalized studies on sectional land-use CEs, which is greatly conductive with the adjustment of land-use structure and further formulation of relevant policies on CE reduction [17].…”

Commentary: Research on the spatial correlation network structure and influencing factors of carbon emissions in Chengdu-Chongqing urban agglomeration

“…At present, studies on the spatial association of carbon emissions between regions can be divided into two categories, one is to use spatial measurement (Tan et al, 2016;Marbuah and Amuakwa-Mensah, 2017;Han et al, 2018) method, such as using SDM model (Min and Tao, 2022), SBM model (Liu et al, 2021;Niu et al, 2022) The spatial dependence of carbon emissions (Xianzhao et al, 2019) and spatial spillover (Shengdong et al, 2022;Xiaoyu et al, 2022) or through spatial autocorrelation models (Zhang and Lei, 2023) spatial autocorrelation model, etc., To characterize the spatial and temporal evolution of carbon emissions (Ahui et al, 2022;Chen et al, 2023). Another approach is to use social network analysis (Tengfei et al, 2022) The other is to use social network analysis to study the structure of carbon emission linkage network, mostly based on national (Ma et al, 2021) and provincial (YANG et al, 2016) energy, industry (Shao and Wang, 2021).…”