Curb Greenhouse Gas Emissions Research Articles

Projected impacts of global warming scenarios on marine ecosystems: Insights from the CMIP6 model

Rising oceanic temperatures and increased heat content have triggered a decline in marine biodiversity and the degradation of ecosystem services. This study employs the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze variables within the Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) framework. Focusing on two contrasting Shared Socioeconomic Pathways (SSPs) - SSP1-2.6 (low-warming scenario) and SSP5-8.5 (high-warming scenario) - we assess future impacts of climate-related factors on marine ecosystems across major oceanic regions. Under the high-warming SSP5-8.5 scenario, global ocean temperature rise, coupled with declining surface pH levels, nitrate concentrations, and plankton biomass/productivity, is evident. Regional reductions in plankton biomass/productivity, especially pronounced at higher latitudes, are observed in both SSP scenarios. Nutrient cycling emerges as a pivotal factor influencing plankton communities, particularly ammonium regulation in the Southern Ocean. This research emphasizes the urgent need to curb greenhouse gas emissions to mitigate global warmings profound effects on marine ecosystems.

Integration of very small modular reactors and renewable energy resources in the microgrid

Hybrid microgrids, integrating local energy resources, present a promising but challenging solution, especially in areas with limited or no access to the national grid. Reliable operation of off-grid energy systems necessitates sustainable energy sources, given the intermittent nature of renewables. While fossil fuel diesel generators mitigate risks, they increase carbon emissions. This study assesses the viability of integrating a very small modular renewable energy reactor into a microgrid for replacing conventional diesel generators, substantially curbing greenhouse gas emissions. A comprehensive analysis, including design and economic evaluation, was conducted for an off-grid community microgrid with an annual generation and load of 8.5 GWh and 7.8 GWh, respectively. The proposed microgrid configurations incorporate very small modular reactors, alongside solar, wind, and battery storage systems. MATLAB modeling and simulation across eight cases, accounting for seasonal variations, demonstrate the technical and economic feasibility of case 7. This configuration, integrating modular reactors, photovoltaics, wind turbines, and battery storage, satisfactorily meets load demands. Notably, it boasts a high internal rate of return up to ∼31% and a shorter payback period of around 4 years compared to alternative scenarios.

Understanding the complex dynamics of climate change in BRICS countries: Analyzing the COP26 and COP27 agendas

The urgency to address climate change becomes increasingly evident as we observe a rise in devastating natural disasters and significant changes in global temperatures. This comprehensive study critically assesses the adherence to climate targets set at COP26 and COP27, employing a dual approach encompassing theoretical and empirical aspects—basic and additional analysis. According to theoretical findings, China, Brazil, and South Africa are still experiencing an increase in climate change indicators despite their collective efforts. Notably, Brazil has shown limited progress in green financing initiatives. Moving to an empirical analysis covering 1995–2021, the study employs advanced econometric techniques, including panel ARDL, FMOLS, DOLS, CS-ARDL, and Grey forecasting models (GM (1,1) and DGM (1,1)). The past data on energy production using both renewable and non-renewable sources spanning from 2010 to 2021 to forecast energy production for the next 8 years, extending up to 2029. Results indicate that green financing, renewable energy consumption, natural resource rents, and government effectiveness significantly reduce GHG emissions. Conversely, economic growth, including its cubic form, exacerbates GHG emission trends. Moreover, the study validates the environmental N-shaped hypothesis in the examined countries, providing a complete understanding of climate change's intricate and multifaceted impacts. The grey forecasting model shows that Brazil, Russia, and South Africa are actively endeavoring to curb greenhouse gas emissions by transitioning toward renewable energy sources for energy production. This research contributes valuable insights for policymakers, emphasizing the importance of targeted interventions in green financing and sustainable practices to effectively address and mitigate climate change.

The Green Breath for the Survival of Mankind in 21st Century using Sustainable Air Purifier

Air pollution, a concern both indoors and outdoors, necessitates effective solutions. While indoor air purifiers gain popularity, outdoor air quality remains crucial. Incorporating solar electricity into outdoor air purification systems offers a sustainable approach. These systems, powered by photovoltaic technology, filter contaminants from outdoor air, mitigating pollution and reducing reliance on non-renewable energy sources. Solar-powered solutions not only enhance air quality but also combat climate change by curbing greenhouse gas emissions. HEPA filters in indoor appliances further improve air quality, especially beneficial for individuals with respiratory conditions like asthma

Exploring the correlation between rising temperature and household electricity consumption: An empirical analysis in China

This study investigates the nexus between rising temperatures and household energy consumption using data from respondents' electricity bills in the "China Residential Energy Consumption Survey." Our analysis reveals a significant correlation, with an 8.9 % increase in yearly energy consumption observed when the average temperature exceeds 32 °C. Additionally, we explore potential shifts in power usage due to global warming by integrating baseline estimates with daily temperature forecasts from eight contemporary climate models. Our findings project alarming trends: without interventions to curb greenhouse gas emissions, home electricity consumption could surge by 9.59–30.09 % in the medium term and by 9.77–47.70 % in the long run. By shedding light on these critical connections, our research underscores the urgent need for policy actions to mitigate the adverse impacts of climate change on energy consumption patterns.

From bricks to bytes: Verifiable data for decarbonizing the building sector

Amidst the pressing need to combat climate change and curb greenhouse gas (GHG) emissions, the building sector emerges as a pivotal sector, substantially impacting worldwide emissions. Despite efforts to improve energy efficiency and incorporate non-fossil energy sources, the sector still lags in achieving the necessary decarbonization goals. Existing Building Energy Management Systems primarily prioritize economic criteria, overlooking the vital aspect of emissions reduction. Energy Informatics and Information Systems hold the potential to bridge this gap by enabling precise and verifiable GHG emissions accounting, end-to-end real-time tracking, and automated verification within Energy Management Systems (EMS). This paper presents research on designing the advancement of EMSs in the form of a Building Energy Emission Management System (BEEMS) leveraging verifiable emission data for emission-based actions. The central research question revolves around designing BEEMS to facilitate emission-based actions based on verifiable data. Following a multi-step approach, the research methodology encompasses a comprehensive literature review and iterative evaluation of our design principles through a workshop and semi-structured interviews with experts from industry and research. The contributions include a conceptual architecture of a BEEMS and six design principles for future BEEMS development. Ultimately, this research strives to facilitate end-to-end verifiable GHG emissions management in the building sector to enable emission-based energy consumption decisions, contributing to the existing body of knowledge of the Energy Informatics field on BEEMS.

Upcycling nitrogen and curbing greenhouse gas emissions from wastewater through H2-driven assimilatory mixotrophic metabolism

Biological nitrogen assimilation is an emerging technique for the removal and upcycling of nitrogen from wastewater in the form of microbial protein (MP). For this purpose, a mixed hydrogen-oxidizing bacteria (HOB) culture capable of growing mixotrophically was evaluated for the treatment of synthetic and real wastewater under different carbon-to-nitrogen (C/N) ratios. The same mixed HOB culture was grown under heterotrophic conditions to compare treatment and process performances in terms of ammonium nitrogen (N-NH4+) removal and assimilation, chemical oxygen demand (COD) removal, CO2 release, biomass concentration, yield and protein content. Under mixotrophic conditions, the highest biomass concentration was 342.5 mg VSS∙L−1, doubling that obtained under heterotrophic conditions (161.2 mg VSS∙L−1). Discharge limits for both COD and total N were met under mixotrophic conditions, with nitrogen removal and assimilation into protein-rich biomass achieving up to 99 %. On the contrary, under heterotrophic conditions, the high content of residual nitrogen as nitrite (up to 26 mg∙L−1 of N-NO2−) did not allow to meet discharge limits for total N. The mixed HOB culture gave promising results in terms of biomass yield (0.32 g VSS·g CODH2+acetate−1) and protein content (up to 56 % of VSS) when grown mixotrophically. Under heterotrophic conditions, instead, the biomass yield (0.25 g VSS·g CODacetate−1) and protein content (35 %) were substantially lower. This study suggests that the H2-driven assimilatory mixotrophic metabolism can be successfully applied for wastewater treatment to produce effluents that meet discharge limits while mitigating greenhouse gas emissions (CO2 and N2O) and upcycling nitrogen into MP.

Combining the role of urbanization and renewable energy in sustainable development: testing the EKC hypothesis for Africa.

The pressing necessity to curb greenhouse gas emissions due to climate change has sparked significant scientific interest in comprehending the factors behind CO2 emissions, particularly concerning environmental sustainability challenges. Nonetheless, there exists a notable gap in our understanding of how the process of urbanization interacts with the utilization of renewable energy to impact CO2 emissions. This research endeavor seeks to evaluate the complex interplay among urbanization, renewable energy, and CO2 emissions across 46 African nations spanning from 1990 to 2019. To accomplish this objective, a variety of econometric methodologies are employed, including Driscoll-Kraay standard errors, IV-GMM, and method of moments quantile regression (MMQR) panel estimations to address issues like cross-sectional dependencies, endogeneity, heterogeneity, and panel Granger causality examination. The empirical results suggest that urbanization leads to an increase in CO2 emissions, whereas the consumption of renewable energy plays a role in enhancing environmental quality by reducing CO2 emissions. A significant outcome of the study is the revelation that a combination of urbanization and renewable energy leads to a decrease in carbon emissions. Moreover, the Environmental Kuznets Curve (EKC) hypothesis is validated. Lastly, through the Dumitrescu-Hurlin panel causality test, it is uncovered that urbanization and renewable energy consumption exhibit a bidirectional relationship with CO2 emissions. To reduce dependence on fossil fuels and curb CO2 emissions, policymakers should promote renewable energy usage in urban areas.

Unveiling the drivers of atmospheric methane variability in Iran: A 20-year exploration using spatiotemporal modeling and machine learning

Understanding the factors controlling spatial and temporal variability of atmospheric methane concentration (XCH4) is crucial for mitigating its impacts and implementing emission reduction strategies. This study comprehensively investigates XCH4 and its driving factors (environmental, meteorological, and anthropogenic activity) across Iran over 20 years, from 2003 to 2022. It combines multi-source satellite observations, advanced spatiotemporal modeling techniques, correlation analysis, and machine learning algorithms. The spatiotemporal analysis showed notable spatial variation, with high XCH4 levels in central, southern, and eastern Iran and lower levels in the northwest and north. Moreover, distinct seasonal cycles emerged, with maximum XCH4 occurring during summer (August-September) and minimum levels in spring (April-May). Correlation analysis and variable importance assessment were developed to elucidate the key drivers governing XCH4 dynamics. Correlation analysis revealed that vegetation cover, precipitation, and soil moisture were negatively correlated with XCH4, while temperature indices showed a positive correlation, exhibiting the highest correlation in time dispersion and quantity among the studied variables. The Permutation Importance technique, used with a Random Forest classifier, a machine learning-based approach that considers the role of all variables together, showed that land surface temperature, wind speed, soil moisture, and vegetation cover are the dominant controls, with their importance ranked respectively. Surprisingly, anthropogenic emissions played a relatively minor role in shaping XCH4 distributions at the regional scale. These findings highlight the significant influence of meteorological variables and ecosystem processes on XCH4 modulation, revealing intricate Earth system feedbacks that inform targeted mitigation strategies and predictive models for curbing greenhouse gas emissions and mitigating climate change impacts.

ELECTRIC VEHICLE UNIFIED WITH WIND AND SOLAR POWER

The global energy crisis and escalating pollution from vehicle emissions pose critical challenges, compelling a shift towards electric vehicles (EVs) as an eco- friendly remedy. Despite their environmental advantages, EVs confront a hurdle in extended charging durations. To combat this, the concept of onboard charging has been explored, where EVs autonomously generate and store electricity. This paper introduces a hybrid model harmonizing solar and wind energies to propel EVs, utilizing the vehicle's battery as a storage unit for surplus energy. Integrating both renewable sources enables continuous energy production, addressing the intermittent nature of solar and wind power. The system strategically prioritizes solar charging during daylight hours and switches to wind charging at night, optimizing energy utilization. Simultaneous usage of both sources enhances overall efficiency. This hybrid system ensures a reliable, sustainable charging solution, reducing dependence on conventional grids and curbing greenhouse gas emissions. The amalgamation of renewable energies into the transportation sector aligns with the global transition towards cleaner and sustainable energy practices. In summary, this hybrid model presents a pragmatic solution to the energy crisis and vehicle emissions, contributing to a greener and more sustainable transportation landscape. Key Words: Hybrid Vehicle, Renewable Energy, Solar power, Wind power, Remote Control, Green transportation, Arduino Uno, Motor Driver, Ultrasonic Sensor.

Collaborative Application of Deep Learning Models for Enhanced Accuracy and Prediction in Carbon Neutrality Anomaly Detection

In the face of intensifying global climate change, carbon neutrality has emerged as a pivotal strategy to curb greenhouse gas emissions and confront the complexities associated with climate challenges. However, achieving carbon neutrality poses a formidable challenge: the identification and mitigation of anomalies within the carbon sequestration process. These anomalies can result in unintended carbon dioxide leakage, emissions, or system failures, thus jeopardizing the feasibility and resilience of carbon neutrality initiatives. This research introduces the ResNet-BIGRU-TPA network, an innovative model that integrates deep learning techniques with time series attention mechanisms. The primary focus centers on addressing the intricate task of anomaly detection within the realm of carbon offsetting, specifically aiming to enhance precision in identifying a wide array of complex anomalous events. Through rigorous experimental validation across four diverse datasets, the model has exhibited exceptional performance.

Reevaluating the Concern of Climate Change

Climate change is inevitably a defining characteristic of this century, which is inattentively attributed to anthropogenic factors far beyond its actualities. The paper challenges the prevailing narrative, proposing that global warming and climate shifts are inherent to Earth's history rather than primarily driven by human activities. It argues that climate change policies impose unwarranted economic strains on nations and impede technological advancement. Scientific assertions of human-induced climate change are scrutinized, with a focus on manipulated data and selective presentation to reinforce the narrative of impending global catastrophe. The exclusive emphasis on curbing greenhouse gas emissions is critiqued for fostering costly and ineffective measures, ultimately stalling economic growth and job creation. The study challenges the prevailing discourse on climate change and socio-economic challenges posed by climate migrations, advocating for a nuanced understanding that considers historical climatic shifts and questions the validity of current research methodologies, encouraging a more comprehensive examination of its multifaceted dynamics and potential societal impacts.

An analysis of environmental and economic impacts of the system of rice intensification : A case study in Thai Binh Province, Vietnam

The current research investigates the environmental and economic impacts of the system of rice intensification. Rice, a fundamental staple in our diets, is paradoxically a significant contributor to greenhouse gas emissions, fueling global warming and climate change. A promising solution to this is the System of Rice Intensification (SRI). Embraced in Vietnam's National Determined Contribution (NDC) under the Paris Agreement, SRI aims to boost rice yields while curbing greenhouse gas emissions compared to conventional farming practices. This study focuses on Thai Binh, a key rice-producing province in the Red River Delta. Our objective is to assess the economic, environmental, and broader societal impacts of SRI versus conventional farming. We conducted a comprehensive analysis, utilizing tools such as Cost and Benefit (CBA) evaluations, Marginal Abatement Cost (MAC) calculations, and the Linkert scale to gauge the effects. Data from 175 farmers in Phu Luong commune, Dong Hung district, Thai Binh province, formed the basis of our study. The results highlight the advantages of adopting SRI. Implementing SRI not only leads to a substantial increase of approximately 12 million Vietnamese Dong (VND) in revenue but also showcases a remarkably favorable cost of -2.7 VND for reducing 1 ton of CO2eq/ha during the transition from conventional farming. This highlights the financial and environmental benefits of SRI. Furthermore, our assessment demonstrates that SRI consistently outperforms conventional farming across economic, environmental, and societal dimensions. In essence, our findings strongly advocate for the adoption of SRI over conventional rice cultivation, as it not only mitigates environmental harm but also enhances farmers' profitability and well-being, aligning with sustainable agriculture practices and climate change mitigation efforts.

Temporally dynamic carbon dioxide and methane emission factors for rewetted peatlands

Rewetting drained peatlands is recognized as a leading and effective natural solution to curb greenhouse gas emissions. However, rewetting creates novel ecosystems whose emission behaviors are not adequately captured by currently used emission factors. These emission factors are applied immediately after rewetting, thus do not reflect the temporal dynamics of greenhouse gas emissions during the period wherein there is a transition to a rewetted steady-state. Here, we provide long-term data showing a mismatch between actual emissions and default emission factors and revealing the temporal patterns of annual carbon dioxide and methane fluxes in a rewetted peatland site in northeastern Germany. We show that site-level annual emissions of carbon dioxide and methane approach the IPCC default emission factors and those suggested for the German national inventory report only between 13 to 16 years after rewetting. Over the entire study period, we observed a source-to-sink transition of annual carbon dioxide fluxes with a decreasing trend of −0.36 t CO2-C ha−1 yr−1 and a decrease in annual methane emissions of −23.6 kg CH4 ha−1 yr−1. Our results indicate that emission factors should represent the temporally dynamic nature of peatlands post-rewetting and consider the effect of site characteristics to better estimate associated annual emissions.

Promoting Green Energy Vehicle: A Global Warming Solution

Given the escalating concerns surrounding global warming, nations worldwide are confronted with the urgent need to curb greenhouse gas emissions. Transitioning to energy-efficient vehicles has emerged as a promising solution to mitigate environmental harm. This paper aims to delve into the present-day dynamics and incentives associated with these eco-friendly vehicles. Specifically, it scrutinizes governmental incentives that promote their acquisition, corporate ingenuity in their manufacturing and distribution, consumer allure factors, and the medias role in portraying both traditional auto industry constraints and affirmative narratives about these sustainable vehicles. Furthermore, the analysis sheds light on predicaments tied to energy vehicles, such as challenges in electricity infrastructure and potential overuse. Findings reveal that prevailing policies across nations remain imperfect, with some strategies focusing on bolstering the effectiveness of green vehicle directives while others are tailored to stimulate consumer buying tendencies. To secure a sustainable vehicular future, an integrated effort among governmental agencies, media outlets, and businesses is paramount.

Carbon taxation in Singapore's semiconductor sector: a mini-review on GHG emission metrics and reporting

The threat of climate change has catalyzed global endeavors to curb greenhouse gas emissions, with carbon taxation emerging as a pivotal policy instrument. Singapore, akin to Taiwan, has embraced this tool, and its ramifications on their semiconductor industry are both profound and multifaceted. At the outset, the imposition of carbon taxes inevitably escalates production costs for semiconductor firms, compelling them to offset their carbon footprint financially. This escalation, in turn, poses a risk of eroding the industry's competitive edge, nudging firms to contemplate the prospect of migrating to locales with more lenient carbon taxation regimes. However, in juxtaposition to these challenges, carbon taxation unveils a silver lining. It instigates semiconductor entities to recalibrate their operations, infusing energy-efficient technologies and pivoting towards renewable energy avenues. Such transitions not only attenuate their carbon emissions but also curtail their financial burden arising from carbon taxation. This manuscript elucidates a panoramic landscape of both policy innovations and technological strides specific to Singapore's semiconductor arena. It aims to be an instrumental compass for stakeholders, delineating pathways for achieving optimal eco-financial equilibrium in the sector.Graphical

Projected changes in heatwaves and its impact on human discomfort over India due to global warming under the CORDEX-CORE framework

Due to climate change, rapid warming and its further intensification over different parts of the globe have been recently reported. This has a direct impact on human health, agriculture, water availability, power generation, various ecosystems, and socioeconomic conditions of the exposed population. The current study thus investigates the frequency and duration of heatwaves, human discomfort, and exposure of the human population to these extremes using the high-resolution regional climate model experiments under two Representative Concentration Pathways (RCP2.6, RCP8.5) over India. We find that more than 90% of India will be exposed to uncomfortable warm nights by the end of the 21st century with the highest rise over western India, Madhya Pradesh (MP), Uttar Pradesh (UP), Punjab, and the Haryana region. States like Odisha, Chhattisgarh, eastern parts of MP and UP, and some parts of J&K will be the worst hit by the intense and frequent heatwaves and human discomfort followed by the densely populated Indo-Gangetic plains under RCP8.5. Strict enforcement of the stringent policies on stabilization of population growth, improvement of local adaptive capacities, and economic status of the vulnerable population along with enforcing effective measures to curb greenhouse gas emissions are important to reduce human exposure to future heat stress. We demonstrate that a proper mitigation-based development (RCP2.6) instead of a business-as-usual scenario (RCP8.5) may help to reduce 50–200 heatwave days, 3–10 heatwave spells, and 10–35% warm nights over the Indian region. Consequently, this can avoid the exposure of 135–143 million population to severe discomfort due to extreme heat conditions by the end of the 21st century.

The Role of Nuclear Energy in Reducing Greenhouse Gas (GHG) Emissions and Energy Security: A Systematic Review

The energy sector accounts for about two-thirds of all human-related greenhouse gas (GHG) emissions due to the reliance on fossil-based fuels. This is a significant concern as it can have dire consequences on the survival of humankind and disrupt other natural processes. The research indicated that some mitigation measures to curb GHG emissions are to increase energy from low-carbon sources such as nuclear. However, due to the continuous adverse climate change impact, a comprehensive systematic review of research in this area must be conducted to inform policy practice and future studies. This study attempts to address this gap by mapping the global reflections on the potential of nuclear technology to mitigate GHG through a bibliometric review process. A total of 741 studies were retrieved from the Scopus database and a few from Google Scholar, spanning from 1962 to 2022, and analyzed using a science mapping tool—VOSviewer. The study confirmed that fossil fuels are a significant source of greenhouse gas emissions and contributor to greenhouse emissions. Those authors concluded that promoting clean and alternative energy sources to fossil fuels would help reduce carbon emissions. Although renewable energy has proven to be very efficient among pollution and climate change mitigation sources, nuclear energy is the most dependable option for meeting national and regional CO2 emission targets while meeting energy supply needs. The bibliometric analysis with VOSviewer suggested that only five African countries, including Ghana, have contributed to the research area with limited collaboration. As a result, it calls for stakeholders to make informed decisions to invest resources in research to address the challenge on the continent. The MESSAGE planning model is recommended for the study.

Optimal combination weight interval-valued carbon price forecasting model based on adaptive decomposition method

Accurate carbon price predictions are of utmost significance in curbing greenhouse gas emissions and advancing environmentally sustainable, low-carbon development. As carbon price time series are influenced by time intervals, point value predictions often fall short in capturing the inherent variability within these intervals. Hence, this paper introduces an interval decomposition and integration framework for carbon price prediction based on the interval feedback variational mode decomposition (IFVMD) algorithm and the optimal combination weight prediction model. First, the IFVMD algorithm decomposes the inverted interval-valued sequence through a self-adaptive determination of the decomposition mode number. It then restructures the decomposed modes into interval trends and residuals, scientifically reducing data volatility. Subsequently, within the proposed optimal combination weight model, the Cuckoo Search Optimization algorithm is employed to calculate the weights of subsequences predicted by two models, which merges model features and realizes model complementation. Finally, the results of the prediction are derived by reconstructing the trend and residual interval. The Interval Mean Absolute Percentage Errors (IMAPE) of the model's prediction results are 2.3754%, 3.3773%, and 3.2169%, respectively. These values indicate that the proposed interval combination forecasting technique demonstrates excellent prediction accuracy and stability, and the performance of the proposed model is superior compared to the baseline models. This optimal combination strategy has been proven to be an effective approach for forecasting nonlinear interval-based carbon price time series.

Designing an acceptable and fair carbon tax: The role of mental accounting

Despite its potential for curbing greenhouse gas emissions, carbon taxation encounters strong public resistance. However, acceptability depends on how tax revenues are used. We test the hypothesis that mental accounting theory can both explain systematic patterns in citizens’ preferences, such as the support for environmental earmarking, and help design a carbon tax scheme that is both acceptable and fair. Across six experiments conducted in the United Kingdom and in France (Ntotal = 7100), we show that: (a) There is an acceptability boost when the use of tax revenues matches the tax domain thematically (e.g., allocating carbon tax revenues to green projects), as demonstrated by an interaction effect between the tax domain and the expenditure domain on the level of tax support. This result is consistent with the use of a mental accounting heuristic, by which people create mental budgets where the origin of revenues is matched thematically with their domain of use. (b) Carbon tax acceptability varies with the proportion of tax revenues earmarked for green projects. (c) A mixed carbon tax scheme, in which most revenues are earmarked for green projects and the rest is redistributed to low-income households to be spent on sustainable expenses, receives most support among the tested options. We also demonstrate the robustness of the mental accounting heuristic in two ways: by showing that the preference for environmental earmarking of carbon taxes is observed across all relevant subsections of the population, and that mental accounting also appears to shape preferences for health-related earmarking of tobacco taxes, and social-related earmarking of inheritance taxes.