Bookkeeping Model Research Articles

Future land carbon removals in China consistent with national inventory

China’s commitment to carbon neutrality by 2060 relies on the Land Use, Land-Use Change, and Forestry (LULUCF) sector, with forestation targets designed to enhance carbon removal. However, the exact sequestration potential of these initiatives remains uncertain due to differing accounting conventions between national inventories and scientific assessments. Here, we reconcile both estimates and reassess LULUCF carbon fluxes up to 2100, using a spatially explicit bookkeeping model, state-of-the-art historical data, and national forestation targets. We simulate a carbon sink of −0.24 ± 0.03 Gt C yr−1 over 1994–2018 from past forestation efforts, aligned well with the national inventory. Should the official forestation targets be followed and extended, this could reach −0.35 ± 0.04 Gt C yr−1 in 2060, offsetting 43 ± 4% of anticipated residual fossil CO2 emissions. Our findings confirm the key role of LULUCF in carbon sequestration, but its potential will decline if forestation efforts cease, highlighting the necessity for emission reductions in other sectors to achieve carbon neutrality.

Attributes of karst lakes in sustaining net autotrophy and carbon sink effects

Natural lakes are significant global sources of CO2 emissions; however, data from lakes in karst regions, which cover 15.2 % of the Earth’s surface, remain insufficiently categorized. This study seeks to elucidate the carbon budget processes and drivers in karst lakes by selecting representative karst and non-karst lakes within the same region. Utilizing high-resolution monitoring, carbon isotope analysis, and mathematical modeling, we investigated the carbon source-sink functions and their controlling factors across different lithologies. Our findings reveal that metabolic processes, quantified using a bookkeeping model, are crucial for driving the diurnal coupling of hydrochemistry and carbon cycling, with karst lakes displaying a pronounced net autotrophic state. Carbon sink fluxes, determined via the boundary layer method, were estimated to be 38 t C km−2 yr−1 for the karst lake, and 11 t C km−2 yr−1 for the non-karst lake. This indicates that despite high dissolved inorganic carbon (DIC) concentrations, the metabolic processes in karst waters, facilitated by their high pH and efficient DIC fertilization, lead to a lower CO2 emission. Furthermore, the low Revelle factor (3.8–4.8) highlights the strong carbonate buffering capacity of karst lakes against CO2. These findings emphasize the capacity of karst lakes to maintain net autotrophy and function as carbon sinks, together with the need to consider lithological differences in future assessments of regional or global lake carbon budgets.

A consistent budgeting of terrestrial carbon fluxes

Accurate estimates of CO2 emissions from anthropogenic land-use change (ELUC) and of the natural terrestrial CO2 sink (SLAND) are crucial to precisely know how much CO2 can still be emitted to meet the goals of the Paris Agreement. In current carbon budgets, ELUC and SLAND stem from two model families that differ in how CO2 fluxes are attributed to environmental and land-use changes, making their estimates conceptually inconsistent. Here we provide consistent estimates of ELUC and SLAND by integrating environmental effects on land carbon into a spatially explicit bookkeeping model. We find that state-of-the-art process-based models overestimate SLAND by 23% (min: 8%, max: 33%) in 2012–2021, as they include hypothetical sinks that in reality are lost through historical ecosystem degradation. Additionally, ELUC increases by 14% (8%, 23%) in 2012–2021 when considering environmental effects. Altogether, we find a weaker net land sink, which makes reaching carbon neutrality even more ambitious. These results highlight that a consistent estimation of terrestrial carbon fluxes is essential to assess the progress of net-zero emission commitments and the remaining carbon budget.

Pelatihan Manajemen Keuangan dalam Pengembangan Usaha Kecil Menengah

The problem found is that the limited ability of small business actors to manage their business finances has resulted in a poor financial managemeny system that does not yet use the accounting bookkeeping model in financial management. This service aims to help small business actors with defficukties in managing their finances. This Community service uses a participatory action based method which is carried out in Bopoma Village, west Golewa District Ngada Regency. The results of activities can provide serious understanding to small and medium business actors about better financial management. This activity process can increase the knowledge of business actors. The conclusions that can be drawn in this training have a positive effect on small business actor to get to know financial cash flow in depth, create simple financial accounting books, hel ensure that business ventures have sufficient funds to meet financial obligations, overcome financial challenges and take advantage of business growth opportunities, in creating a comprehensive budget.

A new estimation of carbon emissions from land use and land cover change in China over the past 300 years

Scientific estimation of carbon emissions induced by historical land use and land cover change (LUCC) can improve the accuracy of terrestrial ecosystem carbon budget estimates and deepen understanding of the future carbon-sink potential of terrestrial ecosystems. The present study, using historical-document-based data for provincial cropland, forest, and grassland area in China, and experimental-data-based information for provincial vegetation and soil organic carbon density, re-estimates China's LUCC-induced carbon emissions for 1700–1980 using a bookkeeping model in which we updated tabulated functions for carbon losses and gains. The past 300 years have witnessed a dramatic LUCC in China. The cropland area has increased by 67.11 million ha, while the forest and grassland areas have decreased by 127.96 million ha and 16.72 million ha, respectively. Accordingly, the net carbon emissions for 1700–1980 are 6.17–12.35 Pg C, with 8.55 Pg C in the moderate scenario. Among the contributing factors, deforestation was the largest carbon source, accounting for over 90 % of the total carbon emissions. According to our estimates, over 70 % of carbon emissions were caused by harvesting wood, while <30 % were from converting forest and grassland to cropland. Spatially, for the whole period, carbon emissions in southwestern China (Chuan-Yu, Yunnan, and Guangxi), northeastern China (Liaoning, Jilin, and Heilongjiang), and parts of northwestern China (Gan-Ning, Qinghai, and Xinjiang) were as high as 6.03 Pg C, accounting for 70 % of the total carbon emissions. Extending previous studies, we updated the historical LUCC data, carbon density data, and tabulated functions for carbon losses and gains. The estimation results objectively reveal the historical spatiotemporal changes in LUCC-induced emissions.

Inconsistent Carbon Budget Estimation Using Dynamic/Static Carbon Density under Land Use and Land Cover Change: A Case Study in Henan Province, China

The scientific estimation of carbon budgets induced by land use and land cover change (LUCC) can improve the accuracy of carbon budget estimates for terrestrial ecosystems and deepen our understanding of the future carbon sink potential of these ecosystems. Previous studies have typically selected one of many LUCC-induced carbon budget methods and overlooked the differences in the results between the different methods. Taking Henan Province, China, as a case study, we used the IPCC method, the bookkeeping model, and the inventory method to estimate LUCC-induced carbon budgets and compared the differences in these methods. The results showed that LUCC in Henan Province was categorized as a carbon sink from 1980 to 2015 based on the IPCC method and the inventory method. However, the estimates were significantly different, and the total estimates of the IPCC method were 40% less than those of the inventory method. At the regional ecogeographical scale, the two methods were not consistent in assessing the carbon source/sink of LUCC. The bookkeeping model categorized LUCC as a carbon source in Henan Province for the same period, and the carbon storage change trend opposed those of the other two methods. The failure of the IPCC method and the bookkeeping model to include the dynamic changes in carbon density caused by climate and land use management led to the great differences between the three methods. The inventory method is recommended to estimate the carbon budget caused by LUCC for regions where carbon density varies greatly over time. Both the IPCC method and the bookkeeping model may have great uncertainty in estimating changes in LUCC-induced carbon stocks and should be used with caution.

Tracking 21st century anthropogenic and natural carbon fluxes through model-data integration

Monitoring the implementation of emission commitments under the Paris agreement relies on accurate estimates of terrestrial carbon fluxes. Here, we assimilate a 21st century observation-based time series of woody vegetation carbon densities into a bookkeeping model (BKM). This approach allows us to disentangle the observation-based carbon fluxes by terrestrial woody vegetation into anthropogenic and environmental contributions. Estimated emissions (from land-use and land cover changes) between 2000 and 2019 amount to 1.4 PgC yr−1, reducing the difference to other carbon cycle model estimates by up to 88% compared to previous estimates with the BKM (without the data assimilation). Our estimates suggest that the global woody vegetation carbon sink due to environmental processes (1.5 PgC yr−1) is weaker and more susceptible to interannual variations and extreme events than estimated by state-of-the-art process-based carbon cycle models. These findings highlight the need to advance model-data integration to improve estimates of the terrestrial carbon cycle under the Global Stocktake.

Land-use change emissions based on high-resolution activity data substantially lower than previously estimated

Land-use and land-cover changes (LULCCs) contributed around one third to the cumulative, anthropogenic CO2 emissions from 1850 to 2019. Despite its great importance, estimates of the net CO2 fluxes from LULCC (ELUC) have high uncertainties, compared to other components of the global carbon cycle. One major source of uncertainty roots in the underlying LULCC forcing data. In this study, we implemented a new high-resolution LULCC dataset (HILDA+) in a bookkeeping model (BLUE) and compared the results to estimates from simulations based on LUH2, which is the LULCC dataset most commonly used in global carbon cycle models. Compared to LUH2-based estimates, results based on HILDA+ show lower total ELUC (global mean difference 1960–2019: 541 TgC yr−1, 65%) and large spatial and temporal differences in component fluxes (e.g. CO2 fluxes from deforestation). In general, the congruence of component fluxes is higher in the mid-latitudes compared to tropical and subtropical regions, which is to some degree explained with the different implementations of shifting cultivation in the underlying LULCC datasets. However, little agreement is reached on the trend of the last decade between ELUC estimates based on the two LULCC reconstructions. Globally and in many regions, ELUC estimates based on HILDA+ have decreasing trends, whereas estimates based on LUH2 indicate an increase. Furthermore, we analyzed the effect of different resolutions on ELUC estimates. By comparing estimates from simulations at 0.01∘ and 0.25∘ resolution, we find that component fluxes of estimates based on the coarser resolution tend to be larger compared to estimates based on the finer resolution, both in terms of sources and sinks (global mean difference 1960–2019: 36 TgC yr−1, 96%). The reason for these differences are successive transitions: these are not adequately represented at coarser resolution, which has the effect that—despite capturing the same extent of transition areas—overall less area remains pristine at the coarser resolution compared to the finer resolution.

Net carbon flux from cropland changes in the Central Asian Aral Sea Basin

The transboundary Aral Sea Basin (ASB) covers parts of the former Union of Soviet Socialist Republics (USSR), the central Asian portion of Kazakhstan (KAZ), Uzbekistan (UZB), Turkmenistan (TUK), Kyrgyzstan (KGZ), and Tajikistan (TAJ). During recent decades, the region has experienced widespread cropland changes and has therefore attracted attention. However, carbon flux caused by these changes, which is critical to understand the carbon cycle in the region and to develop strategies for carbon sequestration, has not been quantified. We applied the Bookkeeping Model to analyze carbon flux caused by cropland changes. We found that the cropland area expanded from 1975 to 2019. Prior to 1990, the net increment in cropland area was 64.47 kha yr−1; this decreased to 11.02–18.69 kha yr−1 after the collapse of the USSR (1991). Grasslands and bare lands with low carbon density were the main types of land that were converted to cropland, accounting for approximately 70.3–99.29% of the land cleared for cropland. In terms of cropland loss, grassland accounted for more than 40.69% of the cropland converted to other land cover types, followed by artificial surfaces, which accounted for 25.84–45.16%. The expansion of cropland served as a carbon sink owing to the increase in irrigation and fertilization. The overall potential carbon emission was −89.38 ± 35.34 Tg C, and contributions of the five countries were as follows: TUR (−33.65 ± 6.30 Tg C), UZB (−29.23 ± 25.35 Tg C), KAZ (−12.76 ± 12.16 Tg C), TAJ (−11.11 ± 5.47 Tg C), KGZ (−2.63 ± 7.83 Tg C). The net carbon flux was −2.04 ± 0.23 Tg C yr−1 before 1990, and it decreased to −1.87 ± 1.03 Tg C yr−1 during the post-Soviet era. Finally, we state sustainable agricultural management is urgently needed to deal with the environmental problems of the Aral Sea, which have been primarily caused by cropland expansion.

Effects of land use and cover change (LUCC) on terrestrial carbon stocks in China between 2000 and 2018

The land-use and cover change (LUCC) is a major factor in the terrestrial carbon cycle. However, the changes of vegetation biomass and soil carbon sequestration caused by land-use conversion have large uncertainties. In the study, we determined the LUCC features through the method of performing transition matrix and transfer trajectory analysis during 2000–2018. By integrating the 3-PGS model and an empirical bookkeeping model, we also explored the carbon budget from land-use conversion in the terrestrial ecosystem of China. The results demonstrated significant losses in grassland (36.22 × 106 ha), farmland (1.39 × 106 ha), and large expansion of built-up land (9.46 × 106 ha), woodland (3.59 × 106 ha). The study revealed that LUCC was driven by natural factors (11.05%) and human activities (8.25%), and natural factors, such as climate, natural vegetation succession, and other natural disturbances, were slightly higher. The total carbon storage caused by LUCC has been increased by approximately 1.32 Pg C, which stored in vegetation biomass (1.11 Pg C) and soil (0.21 Pg C), respectively. Large carbon losses (approximately 29.81 Tg C/yr) have resulted from deforestation, whereas carbon gains happened with the Grain to Green activity, cultivation, and afforestation. Overall, LUCC has a significant effect on increasing sinks in the terrestrial carbon pool in China. Our findings have implications for improving the estimation accuracy of carbon pools and comprehending the impact of human activities on regional carbon balance in China's terrestrial ecosystem.

Carbon fluxes from contemporary forest disturbances in North Carolina evaluated using a grid-based carbon accounting model and fine resolution remote sensing products

Land use/land cover change is a key component in terrestrial carbon cycle, yet there are still large uncertainties in the terrestrial carbon budget. To reduce such uncertainties and refine the spatial distribution of carbon flux, a 30-m Grid-based Carbon Accounting (GCA) model was proposed. We adapted a well-established bookkeeping model into a spatial-explicit model to utilize Landsat time series stacks and to calculate the carbon fluxes resulting from three types of forest disturbances including forest harvesting, forest-to-urban conversion, and fire. Our model results provide spatial details at sub-ha scale that are crucial for carbon management at individual landowner levels. Sensitivity analysis revealed that both pre-disturbance forest carbon and disturbance intensity had large impact on carbon flux estimates arising from forest disturbances that occurred between 1986 and 2010 in North Carolina. At the state level, forest harvesting and fire from 1986 to 2010 released 88.5 MT and 1.6 MT carbon respectively. During the same period, regrowing trees over the logged area absorbed 142.7 MT carbon while those over burned area absorbed 1.6 MT more. The net flux from harvesting, fire, and post-disturbance growth was −52.5 MT. Conversion of forest to urban resulted in a net source of 5.3 MT. Overall, the areas subject to the three types of disturbances and post-disturbance growth was a net sink of 47.2 MT carbon over the entire study period. While our modeling framework was tested at the 30 m spatial resolution in this study, it can be adapted for use with finer spatial and/or temporal resolution remote sensing products that will become more readily available in the coming years, thus further improve the carbon flux estimates.

Fine-Scale Improved Carbon Bookkeeping Model Using Landsat Time Series for Subtropical Forest, Southern China

Subtropical forests easily suffer anthropogenic disturbance, including deforestation and reforestation management, which both highly affect the carbon pools. This study proposes spatial-temporal tracking of the carbon density dynamics to improve bookkeeping in the carbon model and applied to subtropical forest activities in Guangzhou, southern China, during the period of 1995 to 2014. Based on the overall accuracy of 87.5% ± 1.7% for forest change products using Landsat time series (LTS), we found that this is a typical period of deforestation conversion to reforestation activity accompanied with urbanization. Additionally, linear regression, random forest regression and allometric growth fitting were proposed by using forest field plots to obtain reliable per-pixel carbon density estimations. The cross-validation (CV) of random forest with LTS-derived parameters reached the highest accuracy of R2 and RMSE of 0.763 and 7.499 Mg ha−1. The RMES of the density estimation ranged between 78 and 84% of the mean observed biomass in the study area, which outperformed previous studies. Over the 20-year period, the study results showed that the explicit carbon emissions were (6.82 ± 0.26) × 104 Mg C yr−1 from deforestation; emissions increased to (1.02 ± 0.04) × 105 Mg C yr−1 given the implicit carbon not yet released to the atmosphere in the form of decomposing slash and wood products. In addition, a carbon uptake of about 1.91 ± 0.73 × 105 Mg C yr−1, presented as the net carbon pool. Based on the continuous detection capability, biennial reforestation activity has increased carbon density by a growth rate of 1.55 Mg ha−1, and the emission factors can be identified with LTS-derived parameters. In general, the study realizes the spatiotemporal improvement of carbon density and flux dynamics tracking, including the abrupt and graduate change based on fine-scale forest activity. It can provide more comprehensive and detailed feedback on the carbon source and sink change process of forest activities and disturbances.

Contribution of cropland expansion to regional carbon stocks in an arid area of China: a case study in Xinjiang

There has been an increasing number of studies on the potential effects of land-use change on the carbon (C) balance. However, few of these studies have focused on arid regions. Cropland in Xinjiang, a typical arid region in China, has expanded dramatically over the last 40 years. This study applied the Carbon Bookkeeping Model to estimate the changes in C stocks resulting from cropland expansion in Xinjiang from 1975 to 2015. The results showed that the area of cropland increased by a factor of ∼1.6. This increase was driven by advancements in agricultural technology and favorable agricultural policies. The increase in cropland area of 2.03 Mha (M = 106) was the result of the clearing of ∼4.09 Mha land for cropland and the conversion of 2.06 Mha cropland to other land cover types. The expansion in cropland resulted in substantial sequestration of C, with that in Xinjiang amounting to 94.24 Tg C (1Tg = 1012g), accounting for 1.4% of the regional C stocks. Land clearing for cropland (LCC) had the greatest contribution to C sequestration in Xinjiang. The rate of increase in C density through LCC was 0.61 Mg C ha−1a−1 and 1.54 Mg C ha−1a−1 from 1975 to 2004 and 2005 to 2015, respectively. C sequestration due to cropland loss (CLO) of 29.40 Tg C was attributed to the expansion of built-up land and afforestation. Sustainable agricultural activities represented by large-scale clearing for cropland were a major C sink in Xinjiang. Therefore, sustainable management of cropland is essential for maintaining a high C density and preventing loss of C to the atmosphere through cropland abandonment in the future.

Comparison of uncertainties in land-use change fluxes from bookkeeping model parameterisation

Abstract. Fluxes from deforestation, changes in land cover, land use and management practices (FLUC for simplicity) contributed to approximately 14 % of anthropogenic CO2 emissions in 2009–2018. Estimating FLUC accurately in space and in time remains, however, challenging, due to multiple sources of uncertainty in the calculation of these fluxes. This uncertainty, in turn, is propagated to global and regional carbon budget estimates, hindering the compilation of a consistent carbon budget and preventing us from constraining other terms, such as the natural land sink. Uncertainties in FLUC estimates arise from many different sources, including differences in model structure (e.g. process based vs. bookkeeping) and model parameterisation. Quantifying the uncertainties from each source requires controlled simulations to separate their effects. Here, we analyse differences between the two bookkeeping models used regularly in the global carbon budget estimates since 2017: the model by Hansis et al. (2015) (BLUE) and that by Houghton and Nassikas (2017) (HN2017). The two models have a very similar structure and philosophy, but differ significantly both with respect to FLUC intensity and spatiotemporal variability. This is due to differences in the land-use forcing but also in the model parameterisation. We find that the larger emissions in BLUE compared to HN2017 are largely due to differences in C densities between natural and managed vegetation or primary and secondary vegetation, and higher allocation of cleared and harvested material to fast turnover pools in BLUE than in HN2017. Besides parameterisation and the use of different forcing, other model assumptions cause differences: in particular that BLUE represents gross transitions which leads to overall higher carbon losses that are also more quickly realised than HN2017.

Bookkeeping estimates of the net land-use change flux – a sensitivity study with the CMIP6 land-use dataset

Abstract. The carbon flux due to land-use and land-cover change (net LULCC flux) historically contributed to a large fraction of anthropogenic carbon emissions while at the same time being associated with large uncertainties. This study aims to compare the contribution of several sensitivities underlying the net LULCC flux by assessing their relative importance in a bookkeeping model (Bookkeeping of Land Use Emissions, BLUE) based on a LULCC dataset including uncertainty estimates (the Land-Use Harmonization 2 (LUH2) dataset). The sensitivity experiments build upon the approach of Hurtt et al. (2011) and compare the impacts of LULCC uncertainty (a high, baseline and low land-use estimate), the starting time of the bookkeeping model simulation (850, 1700 and 1850), net area transitions versus gross area transitions (shifting cultivation) and neglecting wood harvest on estimates of the net LULCC flux. Additional factorial experiments isolate the impact of uncertainty from initial conditions and transitions on the net LULCC flux. Finally, historical simulations are extended with future land-use scenarios to assess the impact of past LULCC uncertainty in future projections. Over the period 1850–2014, baseline and low LULCC scenarios produce a comparable cumulative net LULCC flux, while the high LULCC estimate initially produces a larger net LULCC flux which decreases towards the end of the period and even becomes smaller than in the baseline estimate. LULCC uncertainty leads to slightly higher sensitivity in the cumulative net LULCC flux (up to 22 %; references are the baseline simulations) compared to the starting year of a model simulation (up to 15 %). The contribution from neglecting wood harvest activities (up to 28 % cumulative net LULCC flux) is larger than that from LULCC uncertainty, and the implementation of land-cover transitions (gross or net transitions) exhibits the smallest sensitivity (up to 13 %). At the end of the historical LULCC dataset in 2014, the LULCC uncertainty retains some impact on the net LULCC flux (±0.15 PgC yr−1 at an estimate of 1.7 PgC yr−1). Of the past uncertainties in LULCC, a small impact persists in 2099, mainly due to uncertainty of harvest remaining in 2014. However, compared to the uncertainty range of the LULCC flux estimated today, the estimates in 2099 appear to be indistinguishable. These results, albeit from a single model, are important for CMIP6 as they compare the relative importance of starting year, uncertainty of LULCC, applying gross transitions and wood harvest on the net LULCC flux. For the cumulative net LULCC flux over the industrial period, the uncertainty of LULCC is as relevant as applying wood harvest and gross transitions. However, LULCC uncertainty matters less (by about a factor of 3) than the other two factors for the net LULCC flux in 2014, and historical LULCC uncertainty is negligible for estimates of future scenarios.

A multi-data assessment of land use and land cover emissions from Brazil during 2000–2019

Brazil is currently the largest contributor of land use and land cover change (LULCC) carbon dioxide net emissions worldwide, representing 17%–29% of the global total. There is, however, a lack of agreement among different methodologies on the magnitude and trends in LULCC emissions and their geographic distribution. Here we perform an evaluation of LULCC datasets for Brazil, including those used in the annual global carbon budget (GCB), and national Brazilian assessments over the period 2000–2018. Results show that the latest global HYDE 3.3 LULCC dataset, based on new FAO inventory estimates and multi-annual ESA CCI satellite-based land cover maps, can represent the observed spatial variation in LULCC over the last decades, representing an improvement on the HYDE 3.2 data previously used in GCB. However, the magnitude of LULCC assessed with HYDE 3.3 is lower than estimates based on MapBiomas. We use HYDE 3.3 and MapBiomas as input to a global bookkeeping model (bookkeeping of land use emission, BLUE) and a process-based Dynamic Global Vegetation Model (JULES-ES) to determine Brazil’s LULCC emissions over the period 2000–2019. Results show mean annual LULCC emissions of 0.1–0.4 PgC yr−1, compared with 0.1–0.24 PgC yr−1 reported by the Greenhouse Gas Emissions Estimation System of land use changes and forest sector (SEEG/LULUCF) and by FAO in its latest assessment of deforestation emissions in Brazil. Both JULES-ES and BLUE now simulate a slowdown in emissions after 2004 (−0.006 and −0.004 PgC yr−2 with HYDE 3.3, −0.014 and −0.016 PgC yr−2 with MapBiomas, respectively), in agreement with the Brazilian INPE-EM, global Houghton and Nassikas book-keeping models, FAO and as reported in the 4th national greenhouse gas inventories. The inclusion of Earth observation data has improved spatial representation of LULCC in HYDE and thus model capability to simulate Brazil’s LULCC emissions. This will likely contribute to reduce uncertainty in global LULCC emissions, and thus better constrains GCB assessments.

Spatiotemporal assessment of land use/land cover change and associated carbon emissions and uptake in the Mekong River Basin

Land use and land cover (LULC) change caused by human activities is a major source of anthropogenic carbon emissions and a driver of climate change. The Mekong Region is highly dynamic, experiencing extensive LULC change in recent decades. This study provides a spatially and temporally continuous estimate of LULC change for the Mekong River Basin for 2001–2019 using time series analysis of MODIS data coupled with a spatiotemporal carbon bookkeeping model to track carbon losses and recovery. The LULC change product has an overall accuracy of 74.4 ± 1.9% (82.1 ± 1.7% after consolidating tree-dominated classes), including an increase of 5.6% after combining with existing MODIS products (referred to as the M-CCDC process). Two of the largest components of LULC change in the region are the establishment of plantations and agricultural expansion, which were estimated to be 33,617 ± 7342 km2 and 14,915 ± 4682 km2 between 2003 and 2014. We found that 82% of the deforested area was converted to tree plantations. Among all the newly added plantations, 86% replaced natural forests and 12% replaced agricultural land. In addition, existing maps of annual tree canopy cover (TCC) were used to assess forest disturbances that do not result in LULC conversions. The M-CCDC results combined with the forest disturbances derived from TCC maps were coupled to a spatiotemporal carbon bookkeeping model to estimate carbon emissions and uptake. Carbon emissions were 72.9 ± 6.2 Tg C yr−1 during 2001–2017; emissions increase to 102.8 ± 8.6 Tg C yr−1 if including carbon not yet released to the atmosphere in the form of decomposing slash and wood products. Carbon uptake for the same period was −35.5 ± 4.9 Tg C yr−1, with carbon uptake from new plantations offsetting almost half of the emissions from deforestation in this area. Assessment of post-deforestation land use is crucial for quantifying the short- and longer- term carbon consequences of LULC change.

The differential motivation and context effects of cause-related marketing in the hotel industry: The moderating role of consumption value

• The interaction between CM message focus and consumption value have a significant effect on consumers’ intent to recommend a hotel. • For hedonic consumption value, intent to recommend CM product is higher through the benefit-focused message than the cost-focused message. • For utilitarian consumption value, intent to recommend CM product is higher through the cost-focused message than the benefit-focused message. • The mediating role of consumers’ motivation (guilt/warm glow) to choose cause-related marketing product was examined. • Warm glow mediates the interaction between message focus and consumption value on consumers’ intention to recommend CM products. This study examined the effects of cause-related marketing (CM: when the consumer is both a donor and a buyer) on the hotel industry. It suggests that based on the double-entry bookkeeping model in which consumers consider the benefits and costs of CM products, two kinds of strategic CM messages were proposed: “benefit-focused” and “cost-focused.” The findings consequently showed that, for hedonic consumption, consumers have a higher intent to recommend CM products when they receive benefit-focused messages as compared to cost-focused messages; however, the opposite was found for utilitarian consumption. Additionally, factors that motivate consumers to choose CM products were investigated. Specifically, with regard to intent to recommend CM products, the respective mediating effects of guilt and “warm glow” on the interaction between message focus and consumption value were examined. Consequently, warm glow, but not guilt, showed a significant mediation effect in this regard.

Historical CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; emissions from land use and land cover change and their uncertainty

Abstract. Emissions from land use and land cover change are a key component of the global carbon cycle. However, models are required to disentangle these emissions from the land carbon sink, as only the sum of both can be physically observed. Their assessment within the yearly community-wide effort known as the “Global Carbon Budget” remains a major difficulty, because it combines two lines of evidence that are inherently inconsistent: bookkeeping models and dynamic global vegetation models. Here, we propose a unifying approach that relies on a bookkeeping model, which embeds processes and parameters calibrated on dynamic global vegetation models, and the use of an empirical constraint. We estimate that the global CO2 emissions from land use and land cover change were 1.36±0.42 PgC yr−1 (1σ range) on average over the 2009–2018 period and reached a cumulative total of 206±57 PgC over the 1750–2018 period. We also estimate that land cover change induced a global loss of additional sink capacity – that is, a foregone carbon removal, not part of the emissions – of 0.68±0.57 PgC yr−1 and 32±23 PgC over the same periods, respectively. Additionally, we provide a breakdown of our results' uncertainty, including aspects such as the land use and land cover change data sets used as input and the model's biogeochemical parameters. We find that the biogeochemical uncertainty dominates our global and regional estimates with the exception of tropical regions in which the input data dominates. Our analysis further identifies key sources of uncertainty and suggests ways to strengthen the robustness of future Global Carbon Budget estimates.

WITHDRAWN: Is HUGO still the BOSS? Investigating the reciprocal effects of brand extensions on brand personality of luxury brands

This research aims to empirically investigate positive and negative reciprocal effects of extensions on brand personality by varying levels of congruency and typicality, while controlling for the effects of motivation processing. A 2 (incongruent, congruent) × 2 (high typicality, low typicality) × 2 (high motivation, low motivation) research design was employed using two extension categories, namely watches and kidswear of the HUGO BOSS brand. The findings revealed brand personality dilution effects only. Specifically, brand personality dilution occurred in response to incongruent and congruent information. Further, contrary to schema theory, brand personality evaluations neither strictly followed the bookkeeping model in high-motivation conditions nor the subtyping model in low-motivation conditions in response to in-congruent information.