Study On The Effect Of Land Use Carbon Emission And The Influencing Factors Under The Peak Carbon Target

With the rapid socio-economic development and urbanization level in China,carbon emissions caused by industrial production and human activities are increasing,and climate warming and disasters are frequent,which bring adverse effects to normal human production and life.How to coordinate the relationship between land use carbon emission and ecological environment and socio-economic development while developing land rationally is the key and difficult point of China’s current urban construction,and the key to achieve the regional carbon peak target as early as possible.This study is based on five phases of remote sensing monitoring data of land use in 11 cities in the Huaihe River Basin(Henan section)in 2000,2005,2010,2015 and 2020,and uses Arc GIS,ENVI and other geo-information statistical methods to analyse and study the spatial and temporal characteristics of carbon emissions in the region from 2000 to 2020 based on carbon emission factors and energy data.With the help of carbon footprint pressure index and carbon emission risk index,the carbon emission evolution effects of various cities in the region are quantified and studied.The grey correlation analysis and LMDI model are then used to explore the main categories and key factors contributing to the increase in carbon emissions.Finally,the STIRPAT model and ridge regression analysis were used to forecast and determine the carbon emissions by scenario for the period 2021-2035 for each city in the study area.The main findings of this study are as follows:(1)In terms of time series,the net carbon emissions in the study area generally show a trend of increasing before decreasing.That is,taking 2015 as the inflection point,it rose from 48,676,800 tonnes in 2000 to 115,033,700 tonnes in 2015,and then decreased to94,752,500 tonnes,with average annual growth rates of 9.1% and-2.3% respectively.from2000 to 2015,both the carbon footprint pressure index and the carbon emission risk index continued to rise,and then began to fall back.In terms of spatial distribution patterns,the distribution characteristics of internal differences in land use carbon emissions in the study area are relatively stable,with a general distribution pattern of high in the centre and low in the surroundings.In other words,the spatial distribution pattern of "Zheng and Luo are dominant and secondary,with decreasing circles" has become more and more obvious over time.(2)During the study period,the correlation coefficients between land use types and carbon emissions in the study area varied significantly,showing an overall trend of increasing,then slowing down and then increasing,with the correlation coefficients of the predominantly carbon source land types being more volatile than those of the carbon sink land types.The influence of each land type on net carbon emissions in the study area is in descending order: construction land,water,forest land,cropland,grassland and unused land.In the LMDI model,the factors of GDP per capita,land use structure,energy structure carbon intensity,and population size play a pulling role in land use carbon emissions over time,while the economic efficiency of land use mainly inhibits the growth of land use carbon emissions during the study period.The overall pulling effect over the 20-year period is characterised by GDP per capita > land use structure > energy structure carbon intensity >population size.(3)Under the baseline scenario and the high economic development scenario,carbon emissions of all cities in the study area show an upward trend from 2021 to 2035,and all cities are unable to reach the 2030 carbon peak target.Under the energy efficiency optimisation scenario,Xuchang and Nanyang achieve peak carbon in 2028,with peaks of6,918,500 tonnes and 7,070,200 tonnes respectively.Zhengzhou,Luoyang,Pingdingshan,Kaifeng,Shangqiu,Luohe,Zhoukou,Xinyang and Zhumadian achieve peak carbon in 2029 with peaks of 14.743 million tonnes,19.336 million tonnes,36.689 million tonnes,3.8643 million tonnes,11.4074 million tonnes,1.5911 million tonnes,2.610 million tonnes,3.9271 million tonnes and 3.974 million tonnes.In order to achieve regional carbon peaking at an early date,it is appropriate to adopt an energy-efficient and optimised development model.