Land-use Carbon Emissions Research Articles

The Coupling Relationship Between Urbanization and Carbon Emissions From Land Use in Ningxia

To understand the complex relationship between urbanization and carbon emissions, this study measures the urbanization level and land-use carbon emissions of 19 counties and districts in Ningxia from 2009 to 2019 using the integrated index method and carbon emission model, respectively, and explores the coupling relationship based on the coupling coordination degree model, to provide theoretical support for low-carbon urbanization and carbon emission reduction planning in Ningxia. The research results showed that the comprehensive evaluation index of the Ningxia urbanization system shows a linear increasing trend with an average annual growth of 1.6%, but the overall level is low, and the overall spatial pattern being high in the North and low in the South is shown. The dominant type of urbanization is population and economic urbanization. The average annual growth rate of net carbon emissions from land use in Ningxia is 4.75 106t/a. Spatially, it is characterized by higher levels in cities in the North along the Yellow River Ecological and Economic Zone and lower levels in the central arid zone and southern mountainous areas. The coupling degree and coupling coordination degree of Ningxia urbanization and land use carbon emissions show a fluctuating upward trend but belong to a low level of mutual constraints, i.e., the low level of development of Ningxia urbanization, backward industries, and low energy utilization lead to the growth of carbon emissions in Ningxia and restrict the development of urbanization. The government should take into account the regional differences when formulating carbon emission reduction policies to maintain appropriate population size, optimize the economic structure, and improve the land use efficiency.

基于格网的南四湖流域土地利用碳排放与其生态系统服务价值时空关系分析

基于格网的南四湖流域土地利用碳排放与其生态系统服务价值时空关系分析

Urbanization, land use change, and carbon emissions: Quantitative assessments for city-level carbon emissions in Beijing-Tianjin-Hebei region

Globally, urbanization has dramatically changed land cover, causing a rapid growth in carbon emissions and related risk of climate change. This study estimated city-level land use carbon emissions (LUCEs) using a novel method based on the correction coefficient calculated by the carbon emissions from energy consumption and basic land use emissions. The method was applied to 13 cities in the Beijing-Tianjin-Hebei (BTH) urban agglomeration in China using 30 m resolution land use data and energy balance tables (EBTs), and the Environmental Kuznets Curve (EKC) was used to discuss the relationship between urbanization and LUCEs in three typical models. The results revealed the expansion of built-up land in the BTH region, and LUCEs at the city-level increase continually, except in Beijing, which showed the most significant expansion of built-up land but a declining trend in LUCEs in recent years. The relationship between urbanization and LUCEs can be summarized into three modes: ‘high urbanization - low emissions’, ‘middle urbanization - high emissions’, and ‘low urbanization - low emissions’. The results have great significance for the formulation of policies to reduce city-level carbon emission at different urbanization levels, and the implementation of high-quality people-oriented new-type urbanization can allow the realization of the carbon emission reduction targets of China.

Reconciling global-model estimates and country reporting of anthropogenic forest CO2 sinks

Achieving the long-term temperature goal of the Paris Agreement requires forest-based mitigation. Collective progress towards this goal will be assessed by the Paris Agreement’s Global stocktake. At present, there is a discrepancy of about 4 GtCO2 yr−1 in global anthropogenic net land-use emissions between global models (reflected in IPCC assessment reports) and aggregated national GHG inventories (under the UNFCCC). We show that a substantial part of this discrepancy (about 3.2 GtCO2 yr−1) can be explained by conceptual differences in anthropogenic forest sink estimation, related to the representation of environmental change impacts and the areas considered as managed. For a more credible tracking of collective progress under the Global stocktake, these conceptual differences between models and inventories need to be reconciled. We implement a new method of disaggregation of global land model results that allows greater comparability with GHG inventories. This provides a deeper understanding of model–inventory differences, allowing more transparent analysis of forest-based mitigation and facilitating a more accurate Global stocktake. The model–inventory discrepancy in net land-use carbon emissions mainly results from conceptual differences in estimating anthropogenic forest sinks. A revised disaggregation of global land model results allows greater comparability with inventories.

Land-Use Carbon Emissions Estimation for the Yangtze River Delta Urban Agglomeration Using 1994–2016 Landsat Image Data

The amount and growth rate of carbon emissions have been accelerated on a global scale since the industrial revolution (1800), especially in recent decades. This has resulted in a significant influence on the natural environment and human societies. Therefore, carbon emission reduction receives continuously increasing public attention and has long been under debate. In this study, we made use of the land-use specific carbon emission coefficients from previous studies and estimated the land-use carbon emissions and carbon intensities of the Yangtze River Delta Urban Agglomeration (YRDUA)—which consists of 26 eastern Chinese cities—from Landsat image data and socio-economic statistics in 1995, 2005, and 2015. In addition, spatial autocorrelation models including both global and local Moran’s I were used to analyze the spatial autocorrelation of carbon emissions and carbon intensities. It was found that (1) the YRDUA witnessed a rapidly increasing trend for net carbon emissions and a decreasing trend for carbon intensity over the two decades; (2) the spatial differences in carbon intensity had gradually narrowed, but were large in carbon emissions and had gradually increased; and (3) the carbon emissions in 2005 and 2015 had significant spatial autocorrelations. We concluded that (1) from 1995 to 2015 in the YRDUA, carbon emissions were large for the cities along the Yangtze River and carbon intensities were high for Anhui province’s resource-based cities, while both carbon emissions and carbon intensities were small for Zhejiang province’s cities; (2) over two decades, the increase in carbon emissions from urban land was approximately twice the increase in urban land area. Our study can provide useful insights into the assignment of carbon reduction tasks within the YRDUA.

A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions

Abstract. A dynamic global vegetation model (DGVM) is applied in a probabilistic framework and benchmarking system to constrain uncertain model parameters by observations and to quantify carbon emissions from land-use and land-cover change (LULCC). Processes featured in DGVMs include parameters which are prone to substantial uncertainty. To cope with these uncertainties Latin hypercube sampling (LHS) is used to create a 1000-member perturbed parameter ensemble, which is then evaluated with a diverse set of global and spatiotemporally resolved observational constraints. We discuss the performance of the constrained ensemble and use it to formulate a new best-guess version of the model (LPX-Bern v1.4). The observationally constrained ensemble is used to investigate historical emissions due to LULCC (ELUC) and their sensitivity to model parametrization. We find a global ELUC estimate of 158 (108, 211) PgC (median and 90 % confidence interval) between 1800 and 2016. We compare ELUC to other estimates both globally and regionally. Spatial patterns are investigated and estimates of ELUC of the 10 countries with the largest contribution to the flux over the historical period are reported. We consider model versions with and without additional land-use processes (shifting cultivation and wood harvest) and find that the difference in global ELUC is on the same order of magnitude as parameter-induced uncertainty and in some cases could potentially even be offset with appropriate parameter choice.

Global land-use allocation model linked to an integrated assessment model.

We developed a global land-use allocation model that can be linked to integrated assessment models (IAMs) with a coarser spatial resolution. Using the model, we performed a downscaling of the IAMs' regional aggregated land-use projections to obtain a spatial land-use distribution, which could subsequently be used by Earth system models for global environmental assessments of ecosystem services, food security, and climate policies. Here we describe the land-use allocation model, discuss the verification of the downscaling technique, and explain the influences of the downscaling on estimates of land-use carbon emissions. A comparison of the emissions estimated with and without downscaling suggested that the land-use downscaling would help capture the spatial distribution of carbon stock density and regional heterogeneity of carbon emissions caused by cropland and pasture land expansion.

Effects of Endogenous Factors on Regional Land-Use Carbon Emissions Based on the Grossman Decomposition Model: A Case Study of Zhejiang Province, China

The impact of land-use change on greenhouse gas emissions has become a core issue in current studies on global change and carbon cycle. However, a comprehensive evaluation of the effects of land-use changes on carbon emissions is very necessary. This paper attempted to apply the Grossman decomposition model to estimate the scale, structural, and management effects of land-use carbon emissions based on final energy consumption by establishing the relationship between the types of land use and carbon emissions in energy consumption. It was shown that land-use carbon emissions increase from 169.5624 million tons in 2000 to 637.0984 million tons in 2010, with an annual average growth rate of 14.15%. Meanwhile, land-use carbon intensity increased from 17.59 t/ha in 2000 to 64.42 t/ha in 2010, with an average annual growth rate of 13.86%. The results indicated that rapid industrialization and urbanization in Zhejiang Province promptly increased urban land and industrial land, which consequently affected land-use extensive emissions. The structural and management effects did not mitigate land-use carbon emissions. By contrast, both factors evidently affected the growth of carbon emissions because of the rigid demands of energy-intensive land-use types and the absence of land management. Results called for the policy implications of optimizing land-use structures and strengthening land-use management.

基于小尺度的高寒牧区土地碳排放估算——以甘南州合作市为例

PDF HTML阅读 XML下载 导出引用 引用提醒 基于小尺度的高寒牧区碳排放估算——以甘南州合作市为例 DOI: 10.5846/stxb201301280176 作者: 作者单位: 西北师范大学地理与环境科学学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然基金地区项目(41261042); 2011年度省属高校基本科研业务费专项资金项目; 2010年度甘肃省高校研究生导师基金项目(1001-22); 西北师范大学青年教师科研能力提升计划-骨干项目(SKQNGG10029) The estimates of carbon emission based on small-scale in alpine pastures: a case study of Hezuo in Gannan Autonomous Prefecture, China Author: Affiliation: Geography and Environmental Science College, Northwest Normal University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:碳排放是目前国内外研究的热点问题.纵观碳排放计算,关键涉及到两个环节:一是研究尺度的选择,二是评估参数(即碳排放/吸收系数)的确定.高寒牧区还必须充分考虑牲畜碳排放.以甘南州合作市为例,将研究尺度缩小到41个行政村,并采用最新的土地二调数据,对土地利用碳排放和碳汇、生活和牲畜碳排放进行了系统地测算.结果表明: (1)从土地利用的角度考察,合作市总碳排放表征为"碳亏"态势.总碳排放量为24374.82 t/a,其中土地利用碳排放量为4908.21 t/a,牲畜代谢碳排放量为3703.94 t/a,城乡居民生活碳排放量为15762.67 t/a.而碳汇量仅为1949.74 t/a,"碳亏"量为22425.08 t/a.(2)牲畜是高寒牧区呼吸碳排放的主体,其碳排量为3703.94 t/a,其中牛是碳排放的主体,其碳排量占90%以上.(3)城乡居民生活碳排量为15762.67 t/a,城镇明显高于农村,生存型碳排放强度高于发展型碳排放强度.(4)碳排放局分布势面与碳汇分布局势面基本呈反向分布态势,但总的碳排放格局取决于碳排放而非碳汇,空间分布表现为从城区-半农半牧区-纯牧区逐级递减的趋势. Abstract:Carbon emissions are the hot issues of the current domestic and international research. Throughout the calculation of carbon emissions, the key involves two aspects: First, the choice of research scale, second, the definition to assessment parameters (carbon emission/absorption coefficient). The alpine pastoral must also full consider Livestock carbon emissions. The paper carried out a systematic calculation of land use carbon emissions and carbon sinks, which combined characteristics of alpine pastures and the lastest second national survey data of land use,by the way of scale-down to the 41 administrative villages. The results show that: (1) The total carbon emissions characterization show as "carbon deficit" situation. The total carbon emissions24374.82t/a, Land-use carbon emissions 4908.21 t/a, the livestock metabolism carbon emissions 3703.94 t/a, and urban-rural residents living carbon emissions 15762.67 t/a. Carbon sinks only have 1949.74 t/a,the amount of "carbon deficit "reaches to 22425.08 t/a. (2)In the alpine pastoral, livestock is the main body of breathing carbon emissions, carbon emissions for 3703.94 t/a, which the cattle are the main factors of the carbon emissions, more than 90% of its carbon emissions.(3)Urban-rural residents living carbon emissions reaches to 15762.67 t/a, the towns significantly higher than rural areas, This suggests that the survival-type carbon emission intensity is higher than the development of carbon emission intensity.(4)From the city-semi-agricultural and semi-pastoral areas-pure pastoral areas, the spatial pattern of carbon emission shows a decreasing trend. 参考文献 相似文献 引证文献

Research on Land Use Carbon Emissions Empirical in Guangdong Province

Based on the land use and land cover changes from 1995 to 2010, the relationship of land use and carbon emissions was estimated, using existing calculate model Change of land use-carbon emissions. The carbon emissions caused by farmland, forest, garden plot, and construction land were estimated, furthermore, the influence of carbon emission was revealed. Finally, the problems that facing G.D. province in the view of carbon reduction was analyzed, and several suggestions was brought out.

Co-Integration Theory-Based Study on Environmental Kuznets Curve of Land Use Change Carbon Emission in Shenzhen

The carbon emission caused by land use change is a human carbon source only second to fossil fuel combustion, the urbanization process in China is extremely intensified, which carbon emissions effect caused by land use change becomes a key factor to influence China's target of carbon emission reduction. Taken Shenzhen city as a typical case, this study utilizes unit root and co-integration test method to research existence of carbon emissions EKC (Environmental Kuznets Curve) in Shenzhen on the basis of the carbon emissions EKC theory and land-use carbon emissions data of 1979-2010 years, in combination with the existing research foundation. The result proves that, the carbon emission EKC per capita exists in Shenzhen, and the inflection point time of carbon emissions per capita is also calculated for Shenzhen city, which provides new research ideas and scientific guidance for urban carbon emission reduction in China.

Linear Programming

Linear Programming