The Net Carbon Emissions from Historic Land Use and Land Use Change

2019 ◽  
Vol 34 (3-4) ◽  
pp. 263-283
Author(s):  
Robert Mendelsohn ◽  
Brent Sohngen
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Carbon Emissions ◽  
Historic Land Use

Carbon emissions from land-use change: an analysis of causal factors in Chiapas, Mexico

2006 ◽  
Vol 12 (6) ◽  
pp. 1213-1235 ◽  
Author(s):  
M. A. Castillo-Santiago ◽  
A. Hellier ◽  
R. Tipper ◽  
B. H. J. de Jong
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Carbon Emissions ◽  
Causal Factors

scholarly journals Smaller global and regional carbon emissions from gross land use change when considering sub-grid secondary land cohorts in a global dynamic vegetation model

2017 ◽  
Author(s):  
Chao Yue ◽  
Philippe Ciais ◽  
Wei Li
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Carbon Emissions ◽  
Shifting Cultivation ◽  
Secondary Forests ◽  
Forest Age ◽  
Priority Rules ◽  
Forest Clearing ◽  
Historical Land Use ◽  
Wood Harvest

Abstract. Several modeling studies reported elevated carbon emissions from historical land use change (LUC) by including bi-directional transitions at the sub-grid scale (termed gross land use change). This has implication on the estimation of so-called residual land CO2 sink over undisturbed lands. However, in most dynamic global vegetation models (DGVM), forests and/or other land use types are represented with a single sub-grid tile, without accounting for secondary lands that are often involved in shifting cultivation or wood harvest. As a result, land use change emissions (ELUC) are likely overestimated, because it is high-biomass mature forests instead of low-biomass secondary forests that are cleared. Here we investigated the effects of including sub-grid forest age dynamics in a DGVM on historical ELUC over 1501–2005. We run two simulations, one with no forest age (Sageless) and the other with sub-grid secondary forests of different age classes whose demography is driven by historical land use change (Sage). Estimated global ELUC for 1501–2005 are 179 Pg C in Sage compared to 199 Pg C in Sageless. The lower emissions in Sage arise mainly from shifting cultivation in the tropics, being of 27 Pg C in Sage against 46 Pg C in Sageless. Estimated cumulative ELUC from wood harvest in the Sage simulation (31 Pg C) are however slightly higher than Sageless (27 Pg C), because secondary forests simulated in Sage are insufficient to meet the prescribed harvest area, leading to the harvest of old forests. This result depends on pre-defined forest clearing priority rules given a simulated portfolio of differently aged forests in the model. Our results highlight that although gross land use change as a former missing emission component is included by a growing number of DGVMs, its contribution to overall ELUC tends to be overestimated, unless low-biomass secondary forests are properly represented.

scholarly journals Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2

2020 ◽  
Vol 13 (7) ◽  
pp. 3203-3220 ◽  
Author(s):  
Lei Ma ◽  
George C. Hurtt ◽  
Louise P. Chini ◽  
Ritvik Sahajpal ◽  
Julia Pongratz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Carbon Emissions ◽  
Forest Cover ◽  
Land Use Changes ◽  
Change Models ◽  
Translation Rule

Abstract. Anthropogenic land-use and land-cover change activities play a critical role in Earth system dynamics through significant alterations to biogeophysical and biogeochemical properties at local to global scales. To quantify the magnitude of these impacts, climate models need consistent land-cover change time series at a global scale, based on land-use information from observations or dedicated land-use change models. However, a specific land-use change cannot be unambiguously mapped to a specific land-cover change. Here, nine translation rules are evaluated based on assumptions about the way land-use change could potentially impact land cover. Utilizing the Global Land-use Model 2 (GLM2), the model underlying the latest Land-Use Harmonization dataset (LUH2), the land-cover dynamics resulting from land-use change were simulated based on multiple alternative translation rules from 850 to 2015 globally. For each rule, the resulting forest cover, carbon density and carbon emissions were compared with independent estimates from remote sensing observations, U.N. Food and Agricultural Organization reports, and other studies. The translation rule previously suggested by the authors of the HYDE 3.2 dataset, that underlies LUH2, is consistent with the results of our examinations at global, country and grid scales. This rule recommends that for CMIP6 simulations, models should (1) completely clear vegetation in land-use changes from primary and secondary land (including both forested and non-forested) to cropland, urban land and managed pasture; (2) completely clear vegetation in land-use changes from primary forest and/or secondary forest to rangeland; (3) keep vegetation in land-use changes from primary non-forest and/or secondary non-forest to rangeland. Our analysis shows that this rule is one of three (out of nine) rules that produce comparable estimates of forest cover, vegetation carbon and emissions to independent estimates and also mitigate the anomalously high carbon emissions from land-use change observed in previous studies in the 1950s. According to the three translation rules, contemporary global forest area is estimated to be 37.42×106 km2, within the range derived from remote sensing products. Likewise, the estimated carbon stock is in close agreement with reference biomass datasets, particularly over regions with more than 50 % forest cover.

scholarly journals Carbon Dioxide Emissions and Their Driving Forces of Land Use Change Based on Economic Contributive Coefficient (ECC) and Ecological Support Coefficient (ESC) in the Lower Yellow River Region (1995–2018)

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2600 ◽  
Author(s):  
Tianqi Rong ◽  
Pengyan Zhang ◽  
Wenlong Jing ◽  
Yu Zhang ◽  
Yanyan Li ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Yellow River ◽  
Yellow River Basin ◽  
Stirpat Model ◽  
Lower Yellow River ◽  
The Lower Yellow River ◽  
The Yellow River Basin

Land use change is the second largest source of greenhouse gas emissions after fossil combustion, which can hurt ecological environment severely. Intensive study on land use carbon emissions is of great significance to alleviate environmental pressure, formulate carbon emission reduction policy, and protect ecological development. The lower Yellow River area is an important area of economic development, grain cultivation, and agricultural production in China. Land use change has significant economic, environmental, and ecological impacts in this region. Deep study of land used carbon emissions and its influencing factors in the lower Yellow River area is not only of great significance to the environmental improvement in the Yellow River basin, but also can provide references for the research of other basins. Based on this, this paper studies the land use carbon emissions of 20 cities in the lower Yellow River area from 1995 to 2018. The results showed that from 1995 to 2018, the land use change was characterized by the decrease of the ecological land and the increase of the built-up land significantly. The overall carbon emission of the lower Yellow River area is increasing, and the built-up land is the main factor that leads to the increase of carbon emission, which can be also proven by the analysis of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. The economic contributive coefficient (ECC) and ecological support coefficient (ESC) of carbon emission in the lower Yellow River area show a trend of high in Zhengzhou, Jinan, and Zibo and low in Zhoukou, Shangqiu, and Heze, and there was no significant changes during the study period, which indicates that each city did not achieve the coordinated development of the ecological economy. Finally, analysis results of the STIRPAT model indicated that the area of built-up land had the greatest impact on land use carbon emissions, followed by tertiary industry, whereas per capita gross domestic product (GDP) had the smallest impact. For every 1% increase in the area of built-up land, carbon emissions increased by 1.024%. By contrast, for every 1% increase in the contribution of tertiary industry to the GDP and per capita GDP, carbon emissions decreased by 0.051% and 0.034%, respectively. According to the study, there are still many problems in the coordinated development of economy and ecology in the lower Yellow River area. The lower Yellow River area should control the expansion of built-up land, afforestation, development of technology, reduction of carbon emissions, and promotion of the high-quality development of the Yellow River Basin.

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