Stock Accumulation Research Articles

Variations in carbon stocks across traditional and improved agroforestry in reference to agroforestry and households' characteristics in Southeastern Ethiopia.

Agroforestry systems help to mitigate climate change by storing carbon in biomass, soils and products. The potential of various homegarden agroforestry (HGAF) practices as a carbon sink in reference to HGAF and household characters has not been adequately studied. Therefore, this study empirically assessed the effects of HGAF practices, HGAF size, HGAF age, and household wealth status on carbon stock accumulation in southeastern Ethiopia. Perennial plant inventory data, litter and soil samples were collected from 96 HGAF farms (48 traditional and 48 improved), consisting of 200 sample plots with a plot size of 20m×20m. A total of 288 soil samples were collected from depths of 0-30cm and 30-60cm to analyze the spatial and vertical distribution of carbon. The results indicated that biomass carbon in improved HGAF practice was about 11.8% higher than in traditional ones. Old age HGAF had 8.7% and 49.4% significantly higher biomass carbon than medium and young age HGAF, respectively. Soil organic carbon varied significantly based on HGAF practices, HGAF size, HGAF age and household wealth status (p<0.05). The mean ecosystem carbon stock of traditional HGAF was by 17.2% higher than that of improved practices. Our results revealed that the potential carbon stocks of the HGAF ecosystem depend on the types of HGAF, household wealth and soil depth. Thus, HGAF should be managed as a viable land use option to help mitigate climate change, while also offering food and income opportunities for smallholder farmers in the studied region and beyond.

Heating up the roof of the world: tracing the impacts of in-situ warming on carbon cycle in alpine grasslands on the Tibetan Plateau.

Climate warming may induce substantial changes in the ecosystem carbon cycle, particularly for those climate-sensitive regions, such as alpine grasslands on the Tibetan Plateau. By synthesizing findings from in-situ warming experiments, this review elucidates the mechanisms underlying the impacts of experimental warming on carbon cycle dynamics within these ecosystems. Generally, alterations in vegetation structure and prolonged growing season favor strategies for enhanced ecosystem carbon sequestration under warming conditions. Whilst warming modifies soil microbial communities and their carbon-related functions, its effects on soil carbon release fall behind the increased vegetation carbon uptake. Despite the fact that no significant accumulation of soil carbon stock has been detected upon warming, notable changes in its fractions indicate potential shifts in carbon stability. Future studies should prioritize deep soil carbon dynamics, the interactions of carbon, nitrogen, and phosphorus cycles under warming scenarios, and the underlying biological mechanisms behind these responses. Furthermore, the integration of long-term warming experiments with Earth system models is essential for reducing the uncertainties of model predictions regarding future carbon-climate feedback in these climate-sensitive ecosystems.

Soil phosphorus compared to nitrogen limitation increases the uncertainty of subsoil organic carbon sequestration in Pinus massoniana mixed forests

Limited nitrogen (N) and phosphorus (P) availability will constrain terrestrial carbon sinks in the 21st century. Mixed forests improve the plant community composition and productivity of pure coniferous forests. Nevertheless, it is uncertain whether and to what extent changes in soil N and P dynamics caused by mixed forests can affect forest soil organic carbon (SOC) stocks. The research purpose is to demonstrate and evaluate the effects of soil N and P on SOC stocks in Pinus massoniana mixed forests. Our meta-analysis, which included 616 paired observations, revealed that the coupling of soil N, P, and plant species richness (PSR) contributed 22.6%, 28.8%, and 28.4%, respectively, to SOC stock accumulation in the topsoil (0–20 cm), subsoil (20–100 cm), and whole profile (0–100 cm). The interactions between soil total N and P concentrations (TN:TP ratios) dominated the increase in SOC stocks in the mixed forest topsoil and whole profile, explaining 35.6% and 20.3% of the variation, respectively. Conversely, independent TN and TP concentrations were the primary contributors (explained by 17.5% and 12.3%, respectively) to subsoil SOC stocks increase. The TN and TP concentrations limit SOC stock accumulation in mixed forests for the next 60 years (2025–2085), with the TP concentration and TN:TP ratio having a greater effect in subsoil. Fortunately, the TN and TP limitations on SOC stock increase in mixed forests can be mitigated and balanced by altering soil TN:TP ratio by increasing or decreasing the PSR (PSR thresholds for topsoil, subsoil, and whole profile were 15, 8, and 8, respectively). Overall, plant mixing fails to enhance plant availability of pure forest soil N and P. Accelerated N cycling and increased P reabsorption efficiency are optimal strategies for balancing N and P supply in mixed forests, promoting biomass accumulation, and ensuring SOC stocks increase.

The Application of Fuzzy Time Series Method for Web-Based Prediction of Household Chemical Product Sales Stock

The seasonal and trend factors of the market demand greatly influence the sales of household chemical products. Therefore, PT. XYZ company requires an accurate sales prediction system to optimize production and inventory. This research aims to develop a web-based sales prediction system for household chemical products using fuzzy time series method. This method considers the factors that influence sales and generates a prediction model that can assist the company in decision-making. This method allows the use of uncertain and unstructured variables in the prediction. The research collected historical sales data of household chemical products from January to December 2023 through interviews, observations, and literature studies. The system was designed using a structured approach and PHP programming language. The Fuzzy Time Series method was used for decision-making. The accuracy of the Fuzzy Time Series method for sales data from January to December 2020 was calculated through program simulation and manual methods, resulting in an accuracy rate of 13.71%. The application provides information on the sales of each product, with the hope that users can reduce the accumulation of stock every month.

Enhancing highland ecosystems in East Hararge, Ethiopia: evaluating intervention strategies for species diversity, soil organic carbon stock, and carbon sequestration

Forest loss and soil degradation pose significant challenges globally, particularly in developing nations like Ethiopia. Various intervention strategies have been implemented to rehabilitate degraded ecosystems. However, research gaps exist in understanding the most effective approaches, particularly in highland ecosystems. This study aimed to evaluate the effectiveness of different intervention strategies on species diversity and carbon stock in the West Hararghe zone, specifically at the Doba district Hades site. The data was collected from 5 February 2022 to 10 December 2022. The study area was characterized by systematic plots and transects to assess vegetation diversity and soil properties across four intervention categories: (physical; only soil and water conservation structures were used, enclosure; only animal and human interaction was restricted, biological; only different tree species were planted, biophysical; both soil water conservation and tree planting were used). Soil samples were collected at different depths, and biomass estimation was conducted using established formulas. Statistical analyses, including one-way ANOVA and post-hoc tests, were performed to test the level of significance. Biophysical interventions exhibited the highest species diversity index (SDI) (4.65 ± 1.21), followed by enclosure interventions (1.68 ± 0.58), while physical interventions recorded the lowest SDI (0.63 ± 0.18). Enclosure interventions led to the highest soil organic carbon (SOC) stocks at 40.13 t C ha−1, followed by biophysical interventions at 39.26 t C ha−1. Physical interventions resulted in the lowest SOC stocks at 23.9 t C ha−1, underscoring their potential for carbon sequestration. Biophysical interventions, which include both above-ground and below-ground biomass, showed significant carbon stock accumulation, totaling 173.8 t C ha−1 (39.26 t C ha−1 from SOC and 114.6 t C ha−1 from biomass), highlighting their effectiveness in enhancing ecosystem resilience. Other studies support these findings, underscoring the importance of context-specific approaches and long-term monitoring for sustainable land management. Enclosure and biophysical interventions emerge as promising strategies for enhancing species diversity and soil health, as well as promoting carbon sequestration in highland ecosystems. These findings emphasize the need for informed decision-making and community involvement in ecosystem restoration efforts to achieve meaningful and lasting impacts.

Logistics optimization and inventory control in Peruvian grocery companies

The company being analyzed, which specializes in selling groceries, faces significant challenges in its logistics management. Lack of supply chain visibility prevents informed inventory decisions, resulting in product overages or shortages. Supply chain delays affect profitability, and inaccurate demand planning generates additional costs and financial losses. This study, which is relevant to the Sustainable Development Goals, mainly in combating poverty through the economic success of SMEs, evaluates the effectiveness of logistics management in inventory control. The results show an increase in ending inventory by 105.12% and a decrease in inventory turnover by 29.29%, suggesting a strategic accumulation of stock. In addition, an improvement in the company's liquidity was observed, with an increase in total current assets by 107.66% and a financial restructuring oriented towards expansion projects. These findings underscore efficient logistics management's importance in optimizing resources and improving customer satisfaction. Future research should focus on the integration of advanced technologies and the implementation of sustainable financing strategies to further improve operational efficiency and profitability.

Artificial intelligence and wealth inequality: A comprehensive empirical exploration of socioeconomic implications

This study introduces a global database on artificial intelligence (AI) capital stock and related AI indicators. Using the data constructed, we investigate the impact of AI and capital stock accumulation on wealth inequality, a dimension not extensively explored in the literature. This study contributes to the growing body of literature on the socioeconomic consequences of AI, with implications for scholars, policymakers, and corporate executives. An innovative database detailing AI capital stock is developed by incorporating data from various sources, including corporate reports, industry databases, and scholarly literature. This novel dataset, focusing on the US, the EU, and Japan from 1995 to 2020, is a critical resource for future investigations. The research methodology is centered on an extended Solow–Swan model, conceptualizing AI as a form of capital that can substitute for or complement traditional forms of labor. A panel-corrected standard errors model is used to analyze the data, accounting for potential cross-sectional dependence and heteroscedasticity. Our findings reveal a positive and statistically significant correlation between AI technology adoption, AI capital stock accumulation, and wealth disparity. The analysis further indicates a complex interaction between income and wealth disparities, suggesting a mutually reinforcing cycle. This study fills a significant gap in the existing literature by offering a novel perspective on the distributional impact of AI. Our results underscore the importance of considering the broader socioeconomic implications of AI, extending beyond considerations of immediate productivity and economic growth. This study offers valuable insights for policy formulation and business decision making, emphasizing the necessity of a comprehensive understanding of the influence of AI on wealth distribution.

Mapping Per- and Polyfluoroalkyl Substance Footprint from Cosmetics and Carpets across the Continental United States.

Per- and polyfluoroalkyl substances (PFAS) released from common consumer products, such as cosmetics and carpets, are nonpoint sources of environmental contamination. However, detailed information on PFAS mass and emission rates from these products is limited. Here, we propose a methodology to develop PFAS footprint from the manufacturing and supply chain data of cosmetics and carpets. Our analysis combines geospatial and statistical assessments to understand how the production and consumption of these products contribute to existing PFAS contamination hotspots in the Continental United States (CONUS). Statewide mass estimations revealed that North Carolina and New York contribute to the major PFAS mass released from cosmetics, while Georgia and California contribute to the major PFAS mass released from carpets. The average per capita PFAS footprint from carpets and cosmetics is about 103 mg/year. Upon disposal, over 60% of the mass eventually ends up in landfills. The accumulation of PFAS stocks in landfills, primarily from carpets and to some extent from cosmetics, highlights the critical need to cease the production and use of PFAS in consumer products. Coastal counties are particularly vulnerable due to higher population and therefore higher consumption of these PFAS-tainted consumer products. Additionally, counties with densely populated areas and with preexisting contamination sources would face increased vulnerability to PFAS contamination released from various consumer products.

Carbon Sequestration Characteristics of Typical Sand-Fixing Plantations in the Shiyang River Basin of Northwest China

A predominant management practice to reduce wind erosion in the arid deserts of northwest China is the planting of shrubs. However, the carbon sequestration capacity of these sand-fixing plantations has not received much attention. In this study, the carbon sequestration capacity of six typical sand-fixing plantations (Haloxylon ammodendron (C. A. Mey.) Bunge, Caragana korshinskii Kom., Tamarix ramosissima Ledeb., Calligonum mongolicum Turcz., Artemisia desertorum Spreng. and Hedysarum scoparium Fisch. &amp; C. A. Mey.) in the Shiyang River Basin were compared and analyzed. We evaluated how carbon sequestration may vary among different species, and examined if plantation age or management style (such as the additional construction of sand barriers, enclosure) positively or negatively influenced the carbon storage potential of these plantation ecosystems. Our results showed that all six plantations could store carbon, but plant species is the controlling factor driving carbon stock accumulation in plantations. The actual organic carbon stored beneath 25-year-old T. ramosissima, H. ammodendron, C. korshinskii, H. scoparium, C. mongolicum and A. desertorum plantations was 45.80, 31.80, 20.57, 20.2, 8.24 and1.76 Mg ha−1, respectively. Plantations using a clay–sand barrier had 1.3 times the carbon sequestration capacity of plantations that only used wheat straw and sand barriers. Similarly, enclosed plantations had 1.4 times the carbon storage capacity of unenclosed plantations. Plantation age greatly impacts carbon sequestration capacity. A 25-year-old H. ammodendron plantation has a carbon sequestration capacity three times greater than that of 3-year plantation. We conclude that while afforesting arid areas, H. ammodendron and T. ramosissima should be prioritized, and priority also should be given to using clay–sand barrier and enclosure.

Carbon stock quantification in a floodplain restoration chronosequence along a Mediterranean-montane riparian corridor

Uncertainty in the global carbon (C) budget has been reduced for most stocks, though it remains incomplete by not considering aquatic and transitional zone carbon stocks. A key issue preventing such complete accounting is a lack of available C data within these aquatic and aquatic-terrestrial transitional ecosystems. Concurrently, quantifiable results produced by restoration practices that explicitly target C stock accumulation and sequestration remain inconsistent or undocumented. To support a more complete carbon budget and identify impacts on C stock accumulation from restoration treatment actions, we investigated C stock values in a Mediterranean-montane riparian floodplain system in California, USA. We quantified the C stock in aboveground biomass, large wood, and litter in addition to the C and total nitrogen in the upper soil profile (5 cm) across 23 unique restoration treatments and remnant old-growth forests. Treatments span 40 years of restoration actions along seven river kilometers of the Cosumnes River, and include process-based (limited intervention), assisted (horticultural planting and other intensive restoration activities), hybrid (a combination of process and assisted actions), and remnant (old-growth forests that were not created with restoration actions) sites. Total C values measured up to 1100 Mg ha−1 and averaged 129 Mg ha−1 with biomass contributing the most to individual plot measurements. From 2012 to 2020, biomass C stock measurements showed an average 32 Mg ha−1 increase across all treatments, though treatment specific values varied. While remnant forest plots held the highest average C values across all stocks (336 Mg ha−1), C values of different stocks varied across treatment type. Process-based restoration treatments held more average biomass C (120 Mg ha−1) than hybrid (23 Mg ha−1) or assisted restoration treatments (50 Mg ha−1), while assisted restoration treatments held more average total C in soil and litter (58 Mg ha−1) than hybrid (35 Mg ha−1) and process-based restoration treatments (37 Mg ha−1). Regardless of treatment type, time was a significant factor for all C stock values. These findings support a more inclusive global carbon budget and provide valuable insight into restoration treatment actions that support C stock accumulation.

Index of profitability of livestock production in Ukraine

Index of profitability of livestock production in Ukraine

Lean Six Sigma to Reduce Dead Stock at PT Globalindo Intimates

PT Globalindo Intimates is a manufacturing company that produces underwear in the garment sector. In the long run, the accumulation of dead stock in the warehouse becomes an unused company asset, making inventory management ineffective and efficient. This study aims to identify and analyze the factors that cause dead stock materials and provide solutions as an improvement effort in minimizing dead stock in the future to increase the effectiveness and efficiency of PT Globalindo Intimates. The method used is Lean Six Sigma with the DMAIC approach model. The data collection technique is done by observation, interview, and documentation. The results of this study explain that the factors that cause dead stock materials are man, method, environment, and material factors, the causes of which include less careful human resources, lack of understanding related to material handling, less than optimal ordering practices, overstock, damaged / defective goods, poor material storage, and low demand. The improvement efforts that can be made to minimize the occurrence of dead stock in the future so that it is not sustainable include making regulations related to the management and handling of dead stock, managing the inventory management system more effectively, optimizing the application of 5S practices in the warehouse, and conducting training for employees

Understanding the Influence of Energy Productivity and Income on CO2 Emissions in Spain (1970–2017): A Non-Linear Structural Equations Model Approach

This study examined the causal relations among per capita CO2 emissions, per capita income, non-fossil fuels electricity production, and energy productivity for the Spanish economy (1970–2017) using structural equation modeling with quadratic effects and introducing the fundamentals of the derived demand to formulate a novel approach. The relationships revealed statistical significance with a scale effect between income per capita and the level of per capita CO2 emissions. There is also a structural effect, dominated by a higher share of non-fossil fuel electricity production, and a technical effect associated with a reduction in energy intensity due to a sustained increase in capital formation and a policy of higher energy prices in the long term. Thus, climate change policies must be based on a sustained effort to modify the energy supply, in addition to an accumulation of capital stock and a sustained policy of keeping relative energy prices, at least in the transition phase to more environment-friendly energy production.

Effect of Vegetation Restoration on Soil Organic Carbon Storage in Coal Mining Areas Based on Meta-analysis

Coal mining is the world's primary means of coping with an increasing energy demand. However, with the mining of coal, the regional ecosystem has been damaged to varying degrees, resulting in a decrease in the "carbon sink" capacity. Vegetation restoration is the basis for the restoration of degraded ecosystems and carbon sequestration functions in mining areas. However, no systematic studies have been conducted on the effects of vegetation restoration on soil organic carbon in coal mining areas on a global scale. Therefore, it is not possible to accurately predict the response of the global SOC pool to vegetation restoration. In this study, soil physicochemical properties of vegetation restoration were collected from 112 peer-reviewed articles to assess the effects of vegetation restoration type, soil depth, restoration year, mean annual temperature, annual precipitation, and elevation on soil organic carbon in coal mining areas and to identify relevant key drivers. The results showed that the damaged coal mine area could significantly improve the physicochemical properties of the soil through vegetation restoration. The restored soils had 39.02% higher SOC reserves compared to that in unrestored or naturally restored soils. When environmental factors were not considered, the vegetation restoration types that were favorable for SOC stock accumulation were cropland > woodland > grassland > shrubland. All four types of vegetation restoration significantly increased the SOC storage in the surface layer (0-20 cm). Grassland and shrubs significantly increased SOC storage at depth (>40 cm), whereas SOC storage at depth under woodland and farmland types was not significantly different from SOC storage after unrestored or natural restoration. The increasing trend of SOC storage after vegetation restoration decreased with increasing soil depth. The specific vegetation restoration strategy should select the appropriate vegetation type according to the climatic conditions. The types of vegetation restoration with higher carbon sequestration effects in damaged coal mining areas with mean annual temperature <0℃ and mean annual precipitation <500 mm were grassland or shrubland. In contrast, woodland and cropland restoration types could better increase SOC storage in environments with mean annual temperature >15℃ and annual precipitation >800 mm. TN, BD, AN, and AK were the main factors influencing the ability to affect soil carbon sequestration. This study can provide a theoretical reference for quantifying the carbon sequestration effects of different vegetation restoration measures in damaged coal mining areas and the restoration and reconstruction of degraded ecosystems.

Enhancing Orchid Inventory Management with K-Means Clustering: A Case Study in Sales Optimization

In the ornamental industry, particularly in orchid sales, manual inventory management has presented significant challenges. These challenges include excessive stock accumulation, financial pressure due to overstocks, and limited land resources to store overstocks. Utilizing information technology, data mining, specifically the K-Means Clustering algorithm, emerges as a solution. This research aims to optimize orchid inventory management by grouping products into three clusters: "highly sellable," "sellable," and "less sellable." Historical sales data was used as the main dataset, considering parameters such as sales volume, demand patterns, and seasonality. The K-Means Clustering algorithm categorized the orchid products, with 97 products in the "highly sellable" cluster, 68 products in the "sellable" cluster, and 22 products in the "less sellable" cluster. This analysis offers insights. Orchids that are "highly marketable" require higher stock levels to meet demand. "Sellable" varieties require standard stock levels, while "less sellable" types should maintain lower stock levels to avoid overstocking. In conclusion, using k-means clustering for orchid inventory management can optimize sales strategies. Orchids in high demand receive adequate stock, ensuring customer satisfaction. Effective stock allocation improves financial stability by overcoming the problem of overstock in the ornamental industry.

Functional diversity and carbon storage of plant community elevation patterns and carbon accumulation mechanisms in desert shrubland of Yanqi Hola Mountain, China

Exploring the distribution characteristics of plant community functional diversity, functional traits and plant and soil carbon stocks along elevation and the effects of functional diversity and functional traits on carbon stocks is of great significance for revealing the distribution characteristics of plant community functional diversity and carbon stocks in the desert area and understanding the carbon accumulation mechanism. In this study, we took the leaf functional traits of plant community species and the carbon stocks of plants and soils of sample plots in the pre-Hola Mountain desert area as the research objects and explored their altitudinal distribution characteristics and their interrelationships. The results showed that (1) the plant carbon storage capacity of the shrub - herb association was the strongest, and the soil carbon storage capacity of the herb association was the strongest. (2) Functional richness and plant carbon storage capacity tended to increase and then decrease with elevation, while functional evenness and leaf phosphorus content tended to decrease and then increase with elevation. Functional dispersion, leaf water content, chlorophyll content, leaf carbon, nitrogen content, and soil carbon stock all showed a gradual increase with elevation, and the total carbon stock gradually increased with elevation in the middle and low elevation areas (1200–1800 m), and showed a wave-like change in the high elevation areas (1800–2400 m). (3) The accumulation of soil carbon stocks in the study area is mainly dominated by the “mass ratio hypothesis”, while the accumulation of plant carbon stocks is mainly dominated by the “ecological niche complementarity hypothesis”, and plant and soil carbon stocks are mainly affected by the specific leaf area and the content of carbon, nitrogen, and phosphorus in leaves.

Comparative Analysis of Fruit Tree–Based Agroforestry and Monoculture in Tackling Climate Change Challenges: Evidence from Sofi District, Ethiopia

Abstract The effects of climate change have hit the agriculture sector in Africa hard. Making adjustments to adapt to the changing environment is critical for countries like Ethiopia, whose primary source of income is subsistence agriculture, which is heavily reliant on rainfall. As a result, establishing context-specific adaptation approaches is crucial for reducing the adverse effects of climate change. This study was conducted at Sofi district, Harari Regional State, Ethiopia, to compare the contribution of fruit tree–based agroforestry and monoculture in tackling climate change challenges. To choose two kebeles and 102 household heads, multistage random sampling was used. Soil and biomass measurements were used to collect soil samples and biomass samples. According to the findings of the study, there are significant differences in how agroforestry and monoculture smallholder farmers deal with climate change challenges in terms of livelihood strategies, with agroforestry smallholders being more resilient. Carbon stock accumulation was estimated to be 453.32 mg ha−1 in agroforestry and 124.7 mg ha−1 in monoculture, where no trees were found on monoculture land. The findings of the study demonstrated that carbon stock accumulation was statistically significant between the mean of soil organic carbon agroforestry land use. Agroforestry systems, in general, contribute significantly more to smallholder livelihoods, as well as the socioeconomic system, in the face of climate change concerns. Incorporating smallholders into an agroforestry system for long-term livelihood sustainability requires support in the form of resources, training, and research. In the face of climate change threats, local, regional, and national governments, as well as financial institutions and other agricultural credit providers, must support smallholders.

Long-term maintenance optimization for integrated mining operations

Maintenance activities are inevitable and costly in integrated mining operations. Conducting maintenance may require the whole system, or sub-units of the system, to be shut down temporarily. These maintenance activities not only disrupt the unit being shut down, but they also have consequences for inventory levels and product flow downstream. In this paper, we consider an interconnected mining system in which there are complicated maintenance relationships and stock accumulation at intermediate nodes. We propose a time-indexed mixed-integer linear programming formulation to optimize the long-term integrated maintenance plan and maximize the total throughput. We also devise an algorithm, which combines Benders decomposition and Lagrangian relaxation, to accelerate the computational speed. To validate our mathematical model, we perform simulations for a real-world case study in the iron ore industry. The results show that our method can yield better solutions than CPLEX optimization solver alone in faster time.

Carbon stocks in the mud areas of the Chinese marginal seas

Continental marginal seas are key systems in the global carbon cycle. Carbon stocks represent the ability to store carbon, thus quantifying the carbon stocks in marine sediments would help to better understand their importance in the carbon cycle. In this study, 17 sediment cores in the mud areas of the South Yellow Sea and the East China Sea were measured for total organic carbon (TOC) and its stable isotope (δ13C), and dry bulk density; and from which the carbon stocks and carbon stock accumulation rate as well as marine/terrestrial carbon stocks/carbon stock accumulation rate were calculated. The carbon stocks in the mud area of the South Yellow Sea showed a decreasing trend during 1855 to 1950 caused by the relocation of the Yellow River Estuary in 1855, but increased after 1950s due to increased sediment input via the enhancement of South Shandong Coastal Current. In the Min-Zhe belt of the East China Sea, carbon stocks showed an overall high marine proportion due to the phytoplankton bloom induced by high nutrient level, but the decreased carbon stocks in recent decades were mainly caused by the construction of reservoirs in the Yangtze River that reduced sediment transports. The average carbon stocks in 1 m sediments from the South Yellow Sea (45.2 t ha-1) and Min-Zhe belt (52.8 t ha-1) were low compared to that of global marine sediments (66.6 t ha-1), while the carbon stock accumulation rate showed much higher values (0.1 t ha-1 yr-1 in South Yellow Sea and 0.31 t ha-1 yr-1 in the Min-Zhe belt) because of higher sedimentation rates. Although carbon stocks of Chinese marginal seas were also lower than that of the tidal flats (70.7 t ha-1) and wetland (123.6 t ha-1) in China, their much larger area could store 0.75 Pg C in marine sediments. Our temporal records suggest that anthropogenic activities have reduced carbon stocks in the marginal seas since 1950, causing carbon to re-enter the atmosphere to impact climate changes.

Analysis of Chemical Inventory Control in the GGCP Unit Using Forecasting and EOQ Methods at PT. XYZ

PT. XYZ is a company operating in the energy producing industry. Which has 2 types of products, steam power and electricity. Steam in GGCP contains 3 chemicals, namely Amine, phosphate, oxygen scavenger. However, this company experienced an accumulation of excessive chemical stocks and reduced chemical consumption. This research aims to determine the number of company orders and determine overall inventory costs using forecasting methods and economical order quantities. In calculating the Total Inventory Cost (TIC) results, there is a significant calculation where TIC EOQ has an economical cost for Amine chemicals of IDR. 15,879,937 while the company's TIC is IDR. 36,226,539. with forecast results for Amine chemicals of 4148/ltr, phosphate of 3061/ltr, and scavenger oxygen of 5450/ltr. Meanwhile, the EOQ calculation results obtained a value for Amine chemicals of 1361/ltr, phosphate of 1164/ltr and 1554/ltr for oxygen scavenger chemicals.