Carbon Price Research Articles

Effect of Different Supplementary Cementitious Materials on Concrete Workability

Supplementary cementitious materials (SCMs)are used for to increase the strength of concrete. The supplementary cementitious materials like fly ash and GGBS (ground-granulated blast-furnace slag) are using in this project. Supplementary cementing materials hardened concrete through pozzolanic activity. In this project the comparing of regular concrete block with Supplementary cementitious materials like fly ash and GGBS (ground-granulated blast-furnace slag). The quantity of fly ash and GGBS is 0%, 10%, 20% and 30%. To compare the block taking the test like workability and strength test. For workability the slump cone test is taken and for strength test the compression test is taken. Portland cement has a high carbon footprint. Using alternative Supplementary cementitious materials for block production might reduce the carbon footprint and price.

Electric vehicle battery closed-loop supply chain pricing and carbon reduction decisions under the carbon cap-and-trade and reward-penalty policies

Recycling end-of-life electric vehicles (EVs) batteries to conserve resources and reduce carbon emissions has obtained a great deal of concern. This paper studied how carbon cap-and-trade and reward-penalty measures jointly impacted EV battery pricing and decarbonization strategies. Three recycling modes covering single-participator, mixed-participator, and joint recycling are established. Optimal pricing and carbon mitigation strategies, total revenue, and recycling percentage are solved and compared. The dynamic effects of target recycling percentage and rewards and punishments on total revenue and recycling percentage are analyzed by numerical examples. Results show that: (1) The factory price, selling price, collection price, and carbon emission mitigation scale of power batteries are affected by cap-and-trade and reward-penalty mechanisms; (2) Reward-penalty can improve both total revenue and recycling percentage; (3) The cap-and-trade mechanism optimizes the total revenues, showing that the total revenue increase with the increasement of carbon quota and carbon price, while the increase of carbon price leads to worse recycling percentage; (4) The supply chain performance under different recycling modes is affected by policy intervention, and the joint recycling mode is better.

A drift-aware dynamic ensemble model with two-stage member selection for carbon price forecasting

Forecasting carbon prices is a pivotal topic in achieving the targets of carbon neutrality and carbon peaking. However, the complex and time-evolving characteristics inherent in carbon price series render precise forecasting a formidable undertaking. Numerous studies have demonstrated that distinct prediction models exhibit varying capabilities and performances, and ensemble learning offers an efficacious approach to enhance forecasting performance. To address variations in model performance and data distribution, a drift-aware ensemble learning framework is employed to adaptively select and combine models for carbon prices forecasting. First, thirty candidate models are generated by integrating data processing techniques with multiple forecast models to comprehensively capture sample information. Second, an initial selection process of candidate models is dynamically executed utilizing a performance drift detection mechanism. Following each drift detection, a second-stage selection is performed given the significance of diversity in ensemble models. Finally, final predictions are calculated by combining the outputs of selected models via a sliding-window weighted average. Carbon price data from four distinct trading markets in China are employed to validate the efficacy of the drift-aware dynamic ensemble (DDE) framework. The results substantiate that DDE can be a convincing tool for the operation and management of carbon trading markets.

An optimal weight heterogeneous integrated carbon price prediction model based on temporal information extraction and specific comprehensive feature selection

Accurate prediction of carbon price provides a strong guarantee for the prosperity of carbon market and the sustainable development of society. This paper proposes an optimal weight heterogeneous integrated carbon price prediction model based on temporal information extraction and specific comprehensive feature selection. Specifically, improved variational mode decomposition is first employed to segment the original carbon price data into distinct sequences, each capturing unique time scale characteristics. Secondly, by comprehensively considering 12 feature screening methods, specifically obtain the optimal impact factors of each subsequence, achieving the purpose of reducing noise interference and improving input quality. Finally, heterogeneous modeling and optimal weight integration, coupled with the northern goshawk optimization, fully leverage the distinct strengths of each model and achieve advantageous complementarity, thereby bolstering the prediction's robustness and precision. The research results demonstrate that, in comparison to the baseline model, the proposed model has achieved remarkable accuracy enhancements in two markets. Taking the Shanghai market as a case, it achieved mean squared error, mean absolute error and mean absolute percentage error values of 1.5199, 0.846, and 0.0145, respectively, with the highest accuracy improvements reaching 43.5555 %, 48.7471 %, and 49.6552 %, respectively. This underscores the outstanding carbon price prediction capability of the model.

THE ROLE OF THE GREEN ECONOMY IN IMPROVING THE STATE BUDGET

THE ROLE OF THE GREEN ECONOMY IN IMPROVING THE STATE BUDGET

Cost-optimal design of reverse electrodialysis process for salinity gradient-based electricity generation in desalination plants

Salinity gradient-based technologies offer a solution for desalination plants seeking clean, uninterrupted electricity to support their decarbonization and circularity. This work provides cost-optimal designs of a large-scale reverse electrodialysis (RED) system deployed in a desalination plant using mathematical programming. The optimization model determines the hydraulic topology and RED units’ working conditions that maximize the net present value (NPV) of the RED process recovering salinity gradient energy between brine and treated wastewater effluents. We examine how electricity, carbon and membranes prices, desalination plant capacity, and membrane resistance may affect the NPV-optimal design’s competitiveness and performance. We also compare the conventional series-parallel configuration and the NPV-optimal solution with recycling and added reuse alternatives. In the context of soaring electricity prices and strong green financing support, with the use of high-performing, affordable membranes (∼10 €/m2), RED could save 8% of desalination plant energy demand from the grid, earning 5 M€ profits and LCOE of 66–126 €/MWh, comparable to other renewable and conventional power technologies. The optimization model finds profitable designs for the entire range of medium-capacity desalination plants. The findings underscore the optimization model effectiveness in streamlining decision-making and exploiting the synergies of full-scale, RED-based electricity in the energy-intensive water sector.

Effect of energy consumption on economic growth and sustainability: Panel evidence from PCSE and FGLS

Energy-growth nexus studies use measures of income to assess the impact of energy consumption on economic growth. However, exclusion of externalities and environmental costs has made these studies a partial one. The recent Energy-Sustainability nexus studies deploy measures of sustainability to assess the impact of energy consumption on sustainable economic welfare rather than just economic growth. The primary objective of the study is to compare the Energy-Growth nexus with Energy-Sustainability nexus for developed countries, developing countries, and transition economies. The time period of the study is from 2000 to 2014. The methodologies used in the study are Feasible Generalized Least Square (FGLS) and Panel Corrected Standard Error (PCSE). The results suggest that renewable energy consumption helps (hurts) economic growth and sustainable development of developed (developing) nations but non-renewable energy consumption hurts (helps) economic growth and sustainable development of developed (developing) nations. However, both renewable and non-renewable energy consumption support economic growth and sustainable development for transition economies. Energy policy recommendations are in favor of strengthening carbon pricing policies, encouraging investment in clean energy technologies, stimulating research and development to overcome challenges of complete energy transition and successful decoupling of economic growth from fossil fuel energy. Moreover, sustainability policies must ensure that economic benefits and welfare arising from renewable energy sources must complement economic growth and sustainable development substantially enough to outweigh the economic costs.

Agricultural carbon credits: A pathway to environmental sustainability

Climate change and ensuring food security for a rapidly growing global population are two of the biggest challenges in agriculture. To meet the commitments made in the Paris Climate Agreement, it is important to use effective methods for managing soil that can help sequester and stabilise carbon. Conservation agriculture has a huge potential to sequester carbon in plants and soil, making it a viable option for carbon trading despite its significant contribution to global greenhouse gas emissions. Carbon sequestration can be achieved through sustainable practices such as adopting conservation agriculture, crop rotation, cover crop cultivation, crop residue incorporation or mulching, effective management of nutrient supply to crops, and transforming towards organic agriculture and agroforestry. These practices promote food security and environmental improvement and help mitigate global warming. Carbon pricing mechanisms are policies that impose a cost on carbon pollution, encouraging people and organisations to choose low-carbon options and reduce their emissions. Agricultural producers can benefit from carbon trading by earning extra revenue by selling their excess carbon credits to those who emit higher amounts of greenhouse gases. Carbon credit systems in agriculture are still in the early stages, so farmers may have more opportunities to participate in future carbon trading.

An interval-valued carbon price prediction model based on improved multi-scale feature selection and optimal multi-kernel support vector regression

An interval-valued carbon price prediction model based on improved multi-scale feature selection and optimal multi-kernel support vector regression

Could industrialization and renewable energy enhance environmental sustainability: An empirical analysis for Sub-Saharan Africa?

Industrialization has led to numerous environmental issues, prompting both industrialized and developing countries to adopt sustainable practices. This research investigates the impact of renewable energy and industrialization on the ecological footprint in Africa using the GMM method with data that span from 2006 to 2020 due to data availability. The findings reveal that renewable energy significantly reduces the level of ecological footprint, while industrialization raises it and thus detrimental to the environment. However, industrialization can enhance environmental sustainability if driven by renewable energy use since renewable energy has the potentials in contributing to sustainable development. It equally helps in mitigating climate change by reducing greenhouse gas emissions and contributing to a cleaner environment. These results therefore underscore the importance of sustainable development and environmental protection in Sub Saharan Africa. The study thus recommends that African countries improve their industrial and institutional frameworks through policies that promote renewable energy investments, support domestic low-carbon technology for producers and phase out fossil fuel subsidies. Additionally, policymakers should implement carbon pricing to incentivize companies to adopt more sustainable practices.

Greening the Corn Belt: Low-carbon-intensity ammonia’s role in the future of sustainable corn production

Ammonia production is a significant source of carbon emissions globally, particularly in the agricultural sector, where it is extensively used as a fertilizer. This research suggests a potential avenue to reduce the carbon footprint of corn production for biofuels in the US Corn Belt. Conventional methods of producing nitrogenous fertilizers derived from ammonia are responsible for the highest CO2 emissions in the life cycle of corn production. We estimate that the total CO2 emissions can be reduced from 3.09 kg CO2 to 2.07 kg CO2 per bushel of corn (33% reduction) when conventional ammonia is replaced with green ammonia. If conventional ammonia is replaced with blue ammonia with carbon capture and storage, the total CO2 emissions can be reduced from 3.09 kg CO2 to 2.27 kg CO2 per bushel of corn (26% reduction). Despite these environmental benefits, our economic analysis reveals that even with policy incentives such as the 45Q and 45V tax credits, low-carbon-intensity ammonia is not yet cost-competitive with conventional ammonia. Renewable (green) ammonia, for instance, remains significantly more expensive at $532 (2024 US Dollar) per metric ton with 45V incentives compared to conventional ammonia at $249 per metric ton. Blue ammonia, even with the 45Q credit, costs $289 per metric ton—still higher than conventional ammonia. However, when a tiered carbon tax of $85 per ton of CO2 is applied to conventional ammonia, raising its cost to $326 per metric ton, blue ammonia becomes cost-competitive, but green ammonia still lags behind. This highlights the need for carbon pricing policies to enable the shift toward lower-carbon ammonia production, as tax credits alone are insufficient to close the cost gap.

Carbon Trading and Environmental Justice: A Juridical Examination of Fairness in Indonesia's Emissions Reduction Initiative

This paper provides a normative juridical analysis of Indonesia's carbon trading regulations, with a focus on environmental justice and fairness. It examines the legal framework established by Presidential Regulation No. 98 of 2021, which governs carbon pricing mechanisms, including carbon taxes and carbon credit trading. The analysis highlights key gaps in the regulation, particularly in terms of equitable benefit distribution and inclusion of vulnerable communities in decision-making processes. While Indonesia's carbon trading system aligns with global trends and its international obligations, significant improvements are needed to ensure that marginalized groups are not further disadvantaged. Recommendations include the redistribution of carbon trading revenues, enhanced public participation, stronger enforcement mechanisms, and alignment with international best practices.

Modeling and Analyzing Carbon Emission Market Volatility and Impact: Evidence from Guangdong Province, China

This research investigates the volatility of carbon prices in Guangdong’s emission trading market, a critical element of China’s broader climate strategy aimed at reducing greenhouse gas emissions and promoting sustainable development. This study applies ensemble empirical mode decomposition (EEMD) to analyze the complex interactions between carbon price fluctuations and various economic factors, including energy prices and environmental regulations. By decomposing the data, we identify key trends and cycles within the market, providing a clearer understanding of both short-term volatility and long-term market trends. Our findings reveal that regulatory policies play a pivotal role in shaping carbon market dynamics, with shifts in regulations leading to significant price volatility. Additionally, fluctuations in global energy prices, especially oil and coal, are found to have a considerable impact on carbon price movements, further complicating the market’s stability. This underscores the interconnected nature of the carbon trading market with broader economic and environmental factors, both domestic and international. The findings provide valuable insights for policymakers and market participants, underscoring the importance of stable carbon markets for promoting the transition to a low-carbon economy and achieving broader sustainability goals.

An Empirical Study of the Economic Net-Zero Energy Mix in Industrial Complexes

This study examines the optimal energy mix for industrial complexes by incorporating renewable energy systems, decarbonization strategies, and sector coupling technologies. Using data from the Balan Industrial Complex in Korea, five energy scenarios were evaluated, ranging from conventional systems (Scenario 1) to advanced renewable configurations (Scenario 5). The results show that Scenario 5, which integrates sector coupling systems and decarbonization technologies, is the most cost-effective and environmentally sustainable. Scenario 5 achieves the lowest Net Present Cost (NPC), and significantly reduces CO2 emissions. Furthermore, an analysis of electricity prices and CO2 costs from Korea, the United States, and Germany highlights the critical role of regional electricity tariffs and carbon pricing in determining the economic feasibility of energy systems. While renewable setups require higher initial investments, Scenario 5 proves to be the most economically viable over time, offering both cost savings and environmental benefits. These findings provide valuable insights for policymakers and industry leaders, emphasizing the importance of customized strategies to optimize energy systems in industrial applications.

Evaluating the effectiveness of climate change policies and initiatives in the United States: A comprehensive review

Climate change presents profound challenges, demanding effective policies to mitigate its impacts. This paper reviews the effectiveness of climate policies in the United States, analyzing scholarly articles, government reports, and relevant literature. The review covers diverse policies such as greenhouse gas regulations, renewable energy initiatives, energy efficiency programs, and adaptation measures. It assesses their success in reducing carbon emissions, advancing a low-carbon economy, and enhancing resilience to climate impacts. Key findings highlight significant successes, such as the rise in renewable energy due to renewable portfolio standards and reduced emissions from vehicle fuel efficiency standards. State-level initiatives, like carbon pricing and renewable energy targets, also show promising outcomes. However, the review identifies substantial challenges, including political and economic obstacles, inconsistent policies across states, and the need for better federal-state-local collaboration. Additionally, disparities in policy benefits, with marginalized communities facing greater burdens and limited access to renewable resources, are significant concerns. To enhance the effectiveness of climate policies, the study recommends fostering coherent policies across all government levels, promoting equitable and inclusive climate solutions, raising public awareness, and supporting innovation in clean technologies. This comprehensive review provides valuable insights for policymakers and stakeholders to develop strategies that effectively address climate change, promote sustainability, and ensure a fair transition to a low-carbon future.

Multisource Data-Driven Carbon Price Composite Forecasting Model: Based on Feature Selection and Multiscale Prediction Strategy

Accurate prediction of carbon trading prices is crucial for guiding investors to make informed decisions and assisting governments in formulating scientific carbon trading policies. This paper introduces a multisource data-driven carbon price composite forecasting model, aimed at enhancing prediction accuracy through advanced data processing and analysis methods. The model initially employs a secondary decomposition strategy, including Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD) methods, to decompose the original data series into three sub-sequences of different frequencies. Subsequently, it utilizes the Partial Autocorrelation Function (PACF) and Random Forest algorithm for feature selection to determine the input variables for different frequency sequences and conducts in-depth analysis and selection of influencing factors, including unstructured data. Furthermore, the model employs a multiscale forecasting strategy, combining Particle Swarm Optimization (PSO) enhanced Bidirectional Long Short-Term Memory (BiLSTM) and Extreme Gradient Boosting (XGBoost) models, along with the Autoregressive Integrated Moving Average (ARIMA) method, to predict based on the characteristics of different frequency components. Finally, the forecasts are integrated using PSO-BiLSTM to form a comprehensive forecast. Notably, given the high correlation between the trend series and influencing factor variables, the model jointly predicts them. A case study based on the Guangdong carbon market in China demonstrates that the proposed composite forecasting model outperforms other benchmark models, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values of 0.4009, 0.2699, and 0.5183%, respectively. This forecasting model provides an effective tool for predicting and analyzing carbon price fluctuations, offering new insights for precise carbon market price predictions.

Addressing companies’ low-carbon transition challenges requires diversified investments in environmental initiatives

The energy, utilities, industrial, and material sectors are crucial suppliers of essential goods and services, but their business operations are among the largest sources of anthropogenic greenhouse gas emissions. Consequently, companies in these sectors play a pivotal role in the low-carbon transition and face substantial stakeholder pressure to manage their transition risks and reduce their environmental impact. Here, we argue that effective responses to transition challenges require diversifying investments in adaptation and mitigation initiatives across a broad range of activities and goals. Analysing financial and nonfinancial data from a global sample of publicly traded companies, we find that those who extensively diversify their investments are better able to reduce their emissions over time. Diversification also reduces carbon pricing risk, thereby lowering exposure to transition risks, under several climate policy scenarios. Our findings provide empirical evidence that business leaders in critical sectors for the low-carbon transition should incorporate well-diversified investments in adaptation and mitigation initiatives into their sustainability strategies to manage interconnected transition challenges.

Are carbon costs transmitting to the building materials industry?

IntroductionReconciling economic development and emission reduction of polluting gases requires balancing long-term and short-term goals across various markets. As a new production cost, the price of carbon allowances is assumed to affect the supply and demand of carbon-intensive sectors. Therefore, this study examines the dynamic price spillover effects between China as the largest carbon emission market and the carbon-intensive building materials industry in a unified time-frequency framework.MethodsThis study measures the dynamic overall and directional spillover effects of carbon and building materials markets in three frequency bands, considering eight carbon pilots and the national carbon market in China and four important building materials.ResultsThe empirical results show that the bi-directional spillover between carbon and the building materials market shows pronounced characteristics in the time-frequency domain, especially in the short-term frequency band of one day to one week, with strong connectivity. After the launch of the national carbon market, the information spillover from the building materials market to the carbon pilots become stronger. Both the carbon pilots and the national carbon market have significant short- and long-term impacts on the building materials market. In addition, there are differences in the impact of carbon markets on various types of carbon intensive building materials.DiscussionCompliance cycles in carbon markets are likely to induce sharp fluctuations in spillovers between the two markets. Therefore, balancing industrial development and stabilizing carbon prices requires a refined policy design that considers the diversified impacts of carbon markets on different industries at across frequencies.

Do Emission Trading Systems, Green Technology, and Environmental Governance Matter for Environmental Quality? Evidence from the European Union

This study investigates how emission trading systems can diminish the ecological footprint in the European Union bloc by considering the contribution of eco-friendly technology and ecological governance through another market-based intervention (i.e., environmental tax). Using PQARDL in the 26 EU members for data during 2011–2021, the study demonstrates that carbon pricing emission trading systems successfully reduce emissions in medium and upper quantiles but have little influence on lower quantiles. As businesses and consumers face higher costs for carbon-intensive activities, they are encouraged to adopt cleaner technologies and more sustainable practices. Over time, this shift decreases overall carbon emissions and resource consumption, which helps lower ecological pressure. Additionally, Europe's robust regulatory framework and commitment to climate goals ensure that carbon pricing is enforced consistently, further amplifying its positive environmental impact. Eco-friendly technology innovation appears to be a powerful tool for achieving rapid environmental advantages, leading to a considerable reduction in environmental footprint across various levels. Climate-smart patents can significantly curb emissions within the ecosystem and help balance the economy and the environment. Environmental taxes work alongside carbon pricing and green patents to incentivize individuals and corporations to adopt more sustainable practices in the 26 EU members. By unraveling the multifaceted relationships underpinning emission trading systems, renewable energy, green patents, and environmental tax, this study provides actionable insights to guide strategic decisions and policy formulations to reduce the ecological footprint.

Greenwashing by the fossil fuel industry: impediments to SDGs

Abstract The fossil fuel industry, a major commercial determinant of public and environmental health, constitutes a central challenge to the achievement of the SDGs. Motivated by profit, the industry has contributed to establishing the current global reliance on fossil fuel as the primary energy source and essential raw material in global production. Major oil companies have purposefully hindered the research and transition towards renewable energy. This was achieved through an intricate web of interference and mechanisms that fall under the corporate playbook, such as political interference through lobbying to stagnate carbon emission reduction, influences on carbon pricing policies, and hinderances to environmental protection legislation. These practices known as ‘greenwashing’ have resulted in misinformation, interfered with climate action in an aim to increase demand for their products, to decrease the pressure from society on the industry, and portray a green image to consumers. Using the commercial determinants of health framework and focusing on the Middle East, this presentation will illustrate how fossil fuel companies falsely advertise their products as environmentally friendly, resulting in environmental degradation, and impede the achievement of most SDGs, especially SDG 3 (good health and wellbeing), SDG 7 (affordable and clean energy), and SDG 13 (climate action).