Future carbon emissions from global mangrove forest loss

Deforestation of temperate rainforests in Chile has decreased the provision of ecosystem services, including watershed protection, biodiversity conservation, and carbon sequestration. Forest conservation can restore those ecosystem services. Greenhouse gas policies that offer financing for the carbon emissions avoided by preventing deforestation require a projection of future baseline carbon emissions for an area if no forest conservation occurs. For a proposed 570 km{sup 2} conservation area in temperate rainforest around the rural community of Curinanco, Chile, we compared three methods to project future baseline carbon emissions: extrapolation from Landsat observations, Geomod, and Forest Restoration Carbon Analysis (FRCA). Analyses of forest inventory and Landsat remote sensing data show 1986-1999 net deforestation of 1900 ha in the analysis area, proceeding at a rate of 0.0003 y{sup -1}. The gross rate of loss of closed natural forest was 0.042 y{sup -1}. In the period 1986-1999, closed natural forest decreased from 20,000 ha to 11,000 ha, with timber companies clearing natural forest to establish plantations of non-native species. Analyses of previous field measurements of species-specific forest biomass, tree allometry, and the carbon content of vegetation show that the dominant native forest type, broadleaf evergreen (bosque siempreverde), contains 370 {+-} 170 t ha{sup -1} carbon, compared to the carbon density of non-native Pinus radiata plantations of 240 {+-} 60 t ha{sup -1}. The 1986-1999 conversion of closed broadleaf evergreen forest to open broadleaf evergreen forest, Pinus radiata plantations, shrublands, grasslands, urban areas, and bare ground decreased the carbon density from 370 {+-} 170 t ha{sup -1} carbon to an average of 100 t ha{sup -1} (maximum 160 t ha{sup -1}, minimum 50 t ha{sup -1}). Consequently, the conversion released 1.1 million t carbon. These analyses of forest inventory and Landsat remote sensing data provided the data to evaluate the three methods to project future baseline carbon emissions. Extrapolation from Landsat change detection uses the observed rate of change to estimate change in the near future. Geomod is a software program that models the geographic distribution of change using a defined rate of change. FRCA is an integrated spatial analysis of forest inventory, biodiversity, and remote sensing that produces estimates of forest biodiversity and forest carbon density, spatial data layers of future probabilities of reforestation and deforestation, and a projection of future baseline forest carbon sequestration and emissions for an ecologically-defined area of analysis. For the period 1999-2012, extrapolation from Landsat change detection estimated a loss of 5000 ha and 520,000 t carbon from closed natural forest; Geomod modeled a loss of 2500 ha and 250 000 t; FRCA projected a loss of 4700 {+-} 100 ha and 480,000 t (maximum 760,000 t, minimum 220,000 t). Concerning labor time, extrapolation for Landsat required 90 actual days or 120 days normalized to Bachelor degree level wages; Geomod required 240 actual days or 310 normalized days; FRCA required 110 actual days or 170 normalized days. Users experienced difficulties with an MS-DOS version of Geomod before turning to the Idrisi version. For organizations with limited time and financing, extrapolation from Landsat change provides a cost-effective method. Organizations with more time and financing could use FRCA, the only method where that calculates the deforestation rate as a dependent variable rather than assuming a deforestation rate as an independent variable. This research indicates that best practices for the projection of baseline carbon emissions include integration of forest inventory and remote sensing tasks from the beginning of the analysis, definition of an analysis area using ecological characteristics, use of standard and widely used geographic information systems (GIS) software applications, and the use of species-specific allometric equations and wood densities developed for local species.

Reliable and accurate environmental state prediction can help in long-term sustainable planning and management. Enormous land-use/ land-cover (LULC) transformation has been increasing the carbon emissions (CEs) and land surface temperature (LST) around the world. The study aimed to (i) examine the influences of land specific CEs on LST dynamics and (ii) simulate future potential LULC, CEs and LST pattern of Khulna City Corporation. Landsat satellite images of the year 2000, 2010 and 2020 were used to derive LULC, LST and CEs pattern and change. The correlation between land-use indices (NDBI, NDVI, NDWI) and LST was examined to explore the impacts of LULC change on LST. Unplanned urbanization has increased 11.79 Km2(26.10%) buildup areas and 25,268 tons of CEs during 2000–2020. The calculated R2 value indicates the strong positive correlation between CEs and LST. To simulate the future LULC, CEs and LST pattern for the year 2030 and 2040, multi-layer perceptron-Markov chain (MLP-MC)-based artificial neural network model was utilized with the accuracy rate of 94.12%, 99% and 98.48% for LULC, LST and CEs model, respectively. The simulation shows that by 2040, buildup area will increase to 87.33%, net CEs will increase by 19.82 × 104tons, and carbon absorptions will decrease by 23. 55 × 104tons and 69.54% of the total study area's LST will be above 390C. Such predictions signify the necessity of implementing a sustainable urban development plan immediately for the sustainable, habitable and sound urban environment.

Current and future carbon emissions from land-use change and energy consumption were analyzed for Sub-Saharan Africa. The energy sector analysis was based on UN energy data tapes while the land-use analysis was based on a spatially-explicit land-use model developed specifically for this project. The impacts of different energy and land-use strategies on future carbon emissions were considered. (A review of anthropogenic emissions of methane, nitrous oxides, and chlorofluorocarbons in Sub-Saharan Africa indicated that they were probably minor in both a global and a regional context. The study therefore was focused on emissions of carbon dioxide.) The land-use model predicts carbon emissions from land use change and the amount of carbon stored in vegetation (carbon inventory) on a yearly basis between 1985 and 2001. Emissions and inventory are modeled at 9000 regularly-spaced point locations in Sub-Saharan Africa using location-specific information on vegetation type, soils, climate and deforestation. Vegetation, soils, and climate information were derived from continental-scale maps while relative deforestation rates(% of forest land lost each year) were developed from country-specific forest and deforestation statistics (FAO Tropical Forest Resources Assessment for Africa, 1980). The carbon emissions under different land use strategies in Sub-Saharan Africa were analyzed by modifying deforestation rates and altering the amount of carbon stored under different land uses. The considered strategies were: preservation of existing forests, implementation of agroforestry, and establishment of industrial tree plantations. 82 refs., 16 figs., 25 tabs.

ABSTRACTThe China's Blue Carbon Plan has made substantial strides since its inception in 2014 and has garnered positive feedback from the domestic surgical technology sector throughout its implementation. This initiative plays a pivotal role in China's goals for carbon peak and carbon neutrality. This paper employs data from the Belt and Road Initiative and 62 Asia‐Pacific countries spanning 2005 to 2021 and applies the PSM‐DID method to empirically assess the impact of China's Blue Carbon Plan on the carbon emissions of Belt and Road and Asia‐Pacific countries. The results show that (1) Blue Carbon Plan can promote carbon emission reduction and per capita carbon emission reduction in the Belt and Road countries and countries in the Asia Pacific region; (2) Blue Carbon Plan can reduce the future carbon emissions of the Belt and Road countries and countries in the Asia Pacific region, but the policy effect shows a certain weakening trend; (3) Blue Carbon Plan can reduce national carbon emissions through four pathways: fishery aquaculture production, fishery fishing production, fishery trade volume and ship fuel consumption. These results provide valuable insights into the effectiveness of blue carbon initiatives and underscore the potential of scaling up such policies to achieve broader regional and global carbon reduction targets.

The BlueFlux field campaign, supported by NASA’s Carbon Monitoring System, will develop prototype blue carbon products to inform coastal carbon management. While blue carbon has been suggested as a nature-based climate solution (NBS) to remove carbon dioxide (CO2) from the atmosphere, these ecosystems also release additional greenhouse gases (GHGs) such as methane (CH4) and are sensitive to disturbances including hurricanes and sea-level rise. To understand blue carbon as an NBS, BlueFlux is conducting multi-scale measurements of CO2 and CH4 fluxes across coastal landscapes, combined with long-term carbon burial, in Southern Florida using chambers, flux towers, and aircraft combined with remote-sensing observations for regional upscaling. During the first deployment in April 2022, CO2 uptake and CH4 emissions across the Everglades National Park averaged −4.9 ± 4.7 μmol CO2 m−2 s−1 and 19.8 ± 41.1 nmol CH4 m−2 s−1, respectively. When scaled to the region, mangrove CH4 emissions offset the mangrove CO2 uptake by about 5% (assuming a 100 year CH4 global warming potential of 28), leading to total net uptake of 31.8 Tg CO2-eq y−1. Subsequent field campaigns will measure diurnal and seasonal changes in emissions and integrate measurements of long-term carbon burial to develop comprehensive annual and long-term GHG budgets to inform blue carbon as a climate solution.

Greenhouse gas inventories from animal agriculture for the United States

Impact of climate and socioeconomic changes on fire carbon emissions in the future: Sustainable economic development might decrease future emissions

Research on future land cover changes and carbon emissions is essential for effective land resource management and developing feasible carbon mitigation strategies. This study focused on the Yellow River Basin and employed the Future Land Use Simulation (FLUS) and Autoregressive Integrated Moving Average (ARIMA) models to project future land cover and carbon emissions. Additionally, bivariate spatial autocorrelation was utilized to analyze the relationship between them. Key findings are as follows: 1) Historically, the Yellow River Basin has experienced an expansion in construction land, forests, grasslands, and water, while cropland and unused land have diminished. Notably, construction land displayed the most significant changes, whereas grasslands showed minimal modification. Looking ahead, both the ecological protection and inertial development scenarios exhibit consistent trends with historical patterns across the land type categories. In contrast, the economic priority development scenario forecasts an increase in construction land, cropland, and grasslands, indicating a distinct shift compared to the other scenarios. However, the ecological protection scenario proves to be more sustainable. 2) In the absence of intervention, the simulated carbon emissions from construction land throughout the basin display a linear increase across various scenarios, with provincial-level variations showing an increase from southwest to northeast. However, Henan and Sichuan are expected to experience slower reductions in carbon emissions, compared to other projections. There is a notable positive correlation between carbon emissions and the comprehensive index, indicating that regions with high emissions typically experience substantial land and economic development. 3) Energy consumption projections for 2030 and 2060 indicate that to align with China’s carbon goals, it is essential to reduce energy consumption and adjust the fossil to non-fossil fuel ratio to reduce carbon emissions. Substituting coal with clean energy and enhancing energy efficiency will be more effective for achieving low-carbon emission targets. In summary, this study provides significant guidance for China’s ecological conservation, low-carbon emission strategies, and global carbon emission control efforts.

Carbon emissions of urban power grid in Jing-Jin-Ji region: Characteristics and influential factors

Under the dual carbon goals, reducing carbon emissions in the planting industry is crucial for achieving green and low-carbon transformation in agriculture. This study focuses on the planting industry in Heilongjiang Province, utilizing the LMDI model, an extended STIRPAT model, and ridge regression to measure carbon emissions from 2002 to 2021, identify influencing factors, and predict future carbon emissions. The results indicate that: ① From 2002 to 2021, carbon emissions showed an overall fluctuating upward trend, divided into four phases: a slow growth period from 2002 to 2004, an accelerated growth period from 2004 to 2016, a fluctuating decline period from 2016 to 2019, and a stable growth period from 2019 to 2021. ② Economic level and agricultural structure promoted carbon emissions, while production efficiency and labor scale inhibited them. ③ Future carbon emissions will maintain a slow growth trend. By 2031, carbon emissions were projected to reach 10.832 million tons, an increase of 593 500 tons compared to that in 2021, with an average annual growth rate of 0.53%. Although Heilongjiang Province has made initial progress in carbon emission reduction, future challenges remain. It is recommended to further develop practical carbon reduction strategies.

Abstract. Mangroves are important for survival of coastal communities as they provide ecosystem services that support coastal population and their livelihoods. Most coastal communities largely depend on ecosystem services provided by mangroves such as fuel wood, building poles, charcoal, and also mangroves provide spawning ground for coastal fishes. Most importantly mangroves act as a buffer that protects coastal communities from natural hazards such as tropical storms, strong winds, beach erosion, and even tsunami. Despite the important role that mangroves play, yet mangroves are under serious threat to extinction worldwide. Many mangrove-rich developing countries, including Tanzania, are facing challenges in establishing effective management plans to protect increasingly threatened mangrove ecosystems. Most of these challenges are associated with inadequate or nonexistent of up-to-date and accurate geospatial information. Knowledge on extent and spatial distribution of mangroves is critical in planning and effective management of mangroves. The aim of this study was to assess the spatial and temporal distribution of mangroves in Mafia Island using remotely sensed data for three decades (1985–2013). Results revealed a decrease of mangroves from 3,708.36 ha in 1985 to 3,187.25 ha in 2013. From the spatiotemporal dimension point of view, the results show that overall mangroves in Mafia Island have been gradually decreasing over time. This trend suggests a decline rate of about 14 % for the period of 28 years, which is an average rate of 0.5 % per year. This rate of mangrove loss should not be underestimated; effective protection measures and sustainable utilization of mangrove resources are needed.

How will ecosystem carbon sequestration contribute to the reduction of regional carbon emissions in the future? analysis based on the MOP-PLUS model framework

The accurate forecasting of agricultural carbon emissions is essential for formulating strategies to achieve carbon peak and neutrality objectives within the agricultural sector. However, existing methodologies for predicting agricultural carbon emissions have notable limitations. To address these shortcomings, Shanghai farm was considered as a case study to conduct research utilizing a neural network approach. Agricultural carbon emissions from the Shanghai farm from 2011 to 2021 were computed using the emission-factor method. Subsequently, a Back Propagation （BP） neural network model was developed to predict carbon emissions, employing the GDP of the planting, animal husbandry, and fishery sectors as input variables. The model was further improved through the application of an optimized sparrow search algorithm, which was then employed to forecast the future carbon emissions of the farm. The results show that the BP neural network improved via the optimized sparrow search algorithm demonstrated a prediction accuracy of 96.14%, a root mean square error （RMSE） of 12 100 t·a-1 and a correlation coefficient （R2） of 0.995 2. These metrics underscored the superior performance of the enhanced model. Compared with the multiple running results of pre-improved models, the neural network improved by the optimized sparrow search algorithm enhanced both the accuracy and stability of carbon emission prediction significantly, with the prediction accuracy consistently approaching approximately 95%, the root mean square error remaining below 20 000 t·a-1, and the correlation coefficient exceeding 0.99. Predictive analysis of future carbon emissions from the Shanghai farm indicated a predominant contribution from the animal husbandry sector to the total carbon emissions, suggesting that effective management of the scale of animal husbandry operations could significantly mitigate overall carbon emissions.

Probabilistic projections of baseline (with no additional mitigation policies) future carbon emissions are important for sound climate risk assessments. Deep uncertainty surrounds many drivers of projected emissions. Here, we use a simple integrated assessment model, calibrated to century-scale data and expert assessments of baseline emissions, global economic growth, and population growth, to make probabilistic projections of carbon emissions through 2100. Under a variety of assumptions about fossil fuel resource levels and decarbonization rates, our projections largely agree with several emissions projections under current policy conditions. Our global sensitivity analysis identifies several key economic drivers of uncertainty in future emissions and shows important higher-level interactions between economic and technological parameters, while population uncertainties are less important. Our analysis also projects relatively low global economic growth rates over the remainder of the century. This illustrates the importance of additional research into economic growth dynamics for climate risk assessment, especially if pledged and future climate mitigation policies are weakened or have delayed implementations. These results showcase the power of using a simple, transparent, and calibrated model. While the simple model structure has several advantages, it also creates caveats for our results which are related to important areas for further research.

China’s agriculture and animal husbandry industry has entered a phase of rapid development. At the same time, the production process of agriculture and animal husbandry generates a large amount of greenhouse gas emissions. This thesis focuses on predicting carbon emissions from agriculture and animal husbandry, i.e., predicting carbon emissions in areas where cultivation and animal husbandry coexist. This thesis assumes that the future carbon emissions from agriculture and animal husbandry in Sichuan Province, China, are predictable. This thesis introduces the regression algorithm in machine learning the carbon emissions of agriculture and animal husbandry, mainly RF (Random Forest Regression Algorithm), and proposes that the combined model of carbon emissions of agriculture and animal husbandry is the FA-ACO-RF model (Factor Analysis-Ant Colony Optimization-Random Forest Regression Algorithm). Empirical evidence of carbon emission prediction in agriculture and animal husbandry was conducted in the Sichuan Province of China as an example, and the results showed that the prediction accuracy reached 99.19%. It is concluded that the FA-ACO-RF model can effectively and accurately predict carbon emissions from agriculture and animal husbandry in Sichuan Province.