Allowance Prices Research Articles

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.

Probability density prediction for carbon allowance prices based on TS2Vec and distribution Transformer

Carbon allowance price is an important tool to reduce carbon emissions and achieve carbon neutrality. It is necessary to establish a predictive model to provide accurate and reliable information to managers and participants in the carbon trading market. Therefore, a novel probability density prediction model, called TS2Vec-based distribution Transformer (TDT), is proposed. TDT consists of two stages: contrastive unsupervised pre-training and supervised training. In the contrastive unsupervised training stage, time series to vector (TS2Vec) is used to represent the dynamic trends and unique features of the data. Then, these representations are fed into the distribution Transformer (DT) to fit the hypothetical probability distribution. Experimental results show that the prediction results of the proposed TDT are more accurate and reliable than other benchmark models. In addition, our research indicates reliable probability density predictions provide enterprises with opportunities to control carbon emission costs and increase economic returns, thereby improving the competitiveness of enterprises and promoting carbon emission reduction.

Carbon market design and market sentiment

Concerns about systematic price distortions in the EU Emission Trading System (ETS) have risen in recent years. This paper shows how carbon-market design affects the impact of market sentiment, i.e. systematic deviations of price expectations from fundamentals by at least some market participants, on equilibrium prices. The Market Stability Reserve (MSR) of the EU ETS that adjusts supply based on past allowance banking undermines self-stabilization of the carbon market by discouraging rational intertemporal arbitrage. The MSR increases vulnerability of the EU ETS to market sentiments. Initially, the allowance price responds more to distorted expectations while the MSR (partially) prevents the distortion to spread across periods. Making the MSR more responsive to banking decisions increases the likelihood that distorted expectations turn out to be correct ex post. In contrast, a mechanism that adjust the cap based on the current allowance price does not face a trade off between stabilizing prices in the present and the future.

The Effectiveness of the EU ETS Policy in Changing the Energy Mix in Selected European Countries

In the field of economic analysis, the study of the EU ETS policy has primarily focused on the impact of renewable energy consumption on economic growth, as well as the role of legal and fiscal instruments in the development of clean energy. This study aimed to evaluate the effectiveness of the EU ETS policy in altering the energy mix of selected European countries, providing both cognitive and applicational value. The evaluation of the effectiveness of this policy focused on the structure of the energy mix and the relationship between rising CO2 emission allowance prices and the decreasing share of coal in the energy mix. The goal was achieved through statistical analysis of secondary sources, primarily sourced from Bloomberg (2016–2024). The research findings indicated that changes in the structure of energy sources varied across the studied European countries, due to the adopted energy source utilization strategy, resource availability, and geopolitical situations. Additionally, different correlation values were noted between rising CO2 emission allowance prices and the expected reduction in fossil fuel use. Therefore, the EU ETS policy does not fulfill its assigned role—its implementation contributes to disparities in the economic situations of European economies and creates conditions for unequal competition.

Carbon Border Adjustment Mechanism (CBAM): Geographical and commodity scope in Polish imports

The Carbon Border Adjustment Mechanism (CBAM) is a new instrument of the EU’s European Green Deal policy. It is intended to contribute to the reduction of greenhouse gas emissions in the world. The CBAM provides for a levy (tax) to be imposed on energy-intensive imports which heavily pollute the environment. The aim of the study was to provide a quantitative assessment of the commodity and geographical coverage of the CBAM in Polish imports. Several indicators were calculated to assess the possible negative impact of CBAM. These indicators can be interpreted as rough measures of the effects of the tax. They were compared with similar indicators calculated by other authors for the whole EU. This paper is a continuation of earlier studies of other researchers who analyzed CBAM coverage and possible implications, while adopting assumptions other than those finally included in the CBAM Regulation. The key findings and main conclusions are as follows: Negative effects of the CBAM will be concentrated on several product groups: steel products, chemicals, polymers, and aluminum. These products accounted for as much as 96% of Poland’s external import subject to the levy. The scale of the final effects of the tax will depend mainly on the level of ETS allowance prices and the intensity of adjustments made by domestic companies and foreign partners. The study ends with two conclusions: First, manufacturers have several years to prepare a strategy to limit the negative effects of the levy (it will be charged as of 2026), but they should start the adjustments as soon as possible. Second, the carbon footprint of a product (as low as possible) is becoming an increasingly important factor in the manufacturers’ international competitiveness. The method of descriptive analysis as well as statistical methods were used.

Emission trading in a high dimensional context: to what extent are carbon markets integrated with the broader system?

The EU ETS represents the cornerstone of the EU climate policy framework. While most of the studies focus on the determinants of carbon price, this work will provide further insights into the influence of European Emission Allowance (EUA) prices on carbon dioxide trends and variables of the economic-financial-climate-environmental system considering a large set of time series. Results highlighted how CO2 appears to be more influenced by commodity prices, climate variables, and past industrial performances. Furthermore, a shock in carbon prices could potentially exert significant turbulence on the carbon dioxide series, fading in intensity as time goes by. Overall, there appears to be a net positive effect on the influence of carbon prices on the system. However, robustness checks identified how the impact of carbon price on CO2 and other variables of the model is still weak. This work sheds light on the EU ETS's influence on a set of multidimensional variables. Still, overlapping national policies appear to interfere with the EU ETS effectiveness in the EU.

Carbon emission allowance price forecasting for China Guangdong carbon emission exchange via the neural network

Carbon emission allowance price forecasting is a significant issue for policy makers and investors with the world transitioning to green energy and devoting enormous efforts to be more sustainable. This study explores usefulness of the nonlinear autoregressive neural network for this forecasting problem in a dataset of daily closing prices of carbon emission allowances traded in China Guangdong Carbon Emission Exchange during 19 December 2013–20 August 2021. Through examining various model settings across the algorithm, delay, hidden neuron, and data splitting ratio, the model leading to generally accurate and stable performance is reached. Usefulness of the machine learning technique for the price forecasting problem of the carbon emission allowance price is illustrated. Results here might be used on a standalone basis as technical forecasts or combined with fundamental forecasts to form perspectives of price trends and perform policy analysis, which could better assist different stakeholders in understanding energy cost and planning for green transition.

Nonlinear Effects of Climate Policy Uncertainty on Carbon Allowance and ESG Prices: Evidence From the US

We examine the nonlinear effects of climate policy uncertainty (CPU) on California carbon allowance prices (CCA) and S&P 500 ESG stock prices (SPESG). We used the nonlinear ARDL method on monthly data from December 2013 to August 2022. Using inflation uncertainty and WTI oil prices as control variables, we found that increases in CPU positively affect carbon allowance and ESG stock prices in the short and long term.

Impact of Climate Policy Uncertainty, Clean Energy Index, and Carbon Emission Allowance Prices on Bitcoin Returns

This research aimed to investigate the relationship between climate policy uncertainty (CPU), clean energy (ENERGY), carbon emission allowance prices (CARBON), and Bitcoin returns (BTC) for the period from August 2012 to August 2022. The empirical analysis strategies utilized in this study included the Fourier Bootstrap ARDL long-term coefficient estimator, the Fourier Granger Causality, and the Fourier Toda–Yamamoto Causality methods. Following the confirmation of cointegration among the variables, we observed a positive relationship between BTC and CARBON, a positive relationship between BTC and CPU, and a negative relationship between BTC and ENERGY. In terms of causal associations, we identified one-way causality running from CARBON to BTC, BTC to CPU, and BTC to the ENERGY variable. The study underscores the potential benefits and revenue opportunities for investors seeking diversified investment strategies in light of climate change concerns. Furthermore, it suggests actionable strategies for policymakers, such as implementing carbon taxes and educational campaigns, to foster a transition towards clean energy sources within the cryptocurrency mining sector and thereby mitigate environmental impacts.

Carbon emission price point-interval forecasting based on multivariate variational mode decomposition and attention-LSTM model

Accurate forecasting of carbon emission prices is a crucial yet challenging task, given the complexity of the trading market and the variability of influencing factors. To comprehensively address factors related to carbon prices while considering interval forecasts associated with uncertainty, this study introduces a novel multistep ahead point-interval forecasting framework for carbon emission prices. This framework integrates multifactor selection, multivariate decomposition and reconstruction, an intelligent point and interval forecasting network, and an evaluation system. First, influencing factors are selected using a recursive feature elimination algorithm based on the extreme gradient boosting estimator. Second, original carbon prices and related influencing factors are decomposed into a set of multimode components (MMCs) using multivariate variational mode decomposition and sample entropy reconstruction techniques. Third, each row of MMCs is predicted using a long short-term memory model with an attention mechanism, and final point predictions are produced through simple addition. Finally, interval predictions are derived using enhanced kernel density estimation (KDE), which can expand the upper and lower bounds of the prediction interval. The stability and robustness have been tested in European Union Allowance (EUA) price forecasting. The results show that the proposed model is superior to other benchmark models, with MAPEs of 0.83%, 3.54%, and 3.82% in 1-step, 15-step, and 30-step ahead forecasting, respectively. Additionally, the proposed enhanced KDE demonstrates excellent performance; in 1-step ahead forecasting, the F-value is 1.86, 1.82, and 1.75 at the 80%, 90%, and 95% confidence levels, respectively. This proves that the proposed framework can effectively improve the point forecasting performance of EUA prices and quantify the prediction uncertainty.

Financial stability and European union allowances: a tale of jumps and regime shifts

ABSTRACT Using an event-study methodology, we investigate the impact of European Union Allowances (EUA) on measures of systemic risks for financial institutions. We analyse daily data on all banks listed on European exchanges. We find statistically significant abnormal changes in systemic risk measures around days when prices of allowances either jumped or shifted regimes. The paper also documents reduced systemic risk around positive jumps and regime shifts of allowances and increased systemic risk throughout negative jumps and regime shifts. Our contribution to the literature consists in producing evidence that financial stability supports the European carbon market initiative to mitigate climate change. These results have significant implications for policymakers and financial institutions.

Pricing of Chinese Certified Emissions Reduction Scheme in a Supply Chain Under the Cap-and-Trade Policy

Abstract Against the backdrop of carbon trading, the Chinese Certified Emissions Reduction (CCER) scheme is one of the most important measures of China to reduce carbon emissions. We study a supply chain consisting of a CCER supplier and a manufacturer that buys CCER quotas from the supplier. The supplier is a non-emissions-control enterprise engaged in clean energy projects, while the manufacturer is classified into three types: a clean, a relatively clean, and an unclean firm. We analyse the impacts of the price and offset rate of CCER quotas on firms’ emissions reductions. We find that when the price of CCER quotas is lower than the price of Chinese Emissions Allowances (CEAs): (1) for both clean and unclean manufacturers, with increasing CCER offset rate (quota price), technical emissions reduction decreases (increases). (2) The incentive for the relatively clean manufacturer to reduce emissions is not affected by other extraneous factors. When the prices of CCER quotas and CEAs are uncertain, we find that (1) the technology abatement decisions of the three types of manufacturer are consistent and the corresponding CCER suppliers price their CCER quotas in the same way. (2) When the proportion of CCER quotas purchased by the manufacturer increases, the unit technical emissions reduction increases. Confirming that introduction of the CCER scheme can reduce carbon emissions, our findings provide theoretical guidance for suppliers to make pricing decisions on CCER quotas and for manufacturers to make technical emissions reduction decisions.

Implications of the Carbon Border Adjustment Mechanism on South Korean industries: Challenges and policy recommendations

This study examines the effects of the European Union's Carbon Border Adjustment Mechanism (CBAM) on South Korean industries. Concerns about carbon leakage, where carbon emissions increase in countries or regions with less stringent climate policies, have grown recently. Specifically, firms strategically relocate their operations to countries with lower carbon costs, where they can produce goods more cheaply and without carbon constraints. CBAM is a carbon tariff on carbon intensive products imported by the European Union. While the CBAM has the potential to reduce global carbon emissions, it poses significant risks to South Korea, which relies heavily on exports of CBAM-affected products. This study provides an overview of the potential effects of the CBAM on industries in South Korea, applies event study analysis to quantitatively evaluate how the financial market has reacted to CBAM-related events. Based on the research findings, as the details of CBAM become clearer, there is a shift in perception from environmental goals to broader issues across industries. Building on this understanding, we discuss issues related to the CBAM enactment: firm-level carbon accounting, EC-certified accreditation institutions, and the carbon allowance price gap between the EU and South Korea. We propose necessary short-term and long-term policies to attenuate CBAM-related risks.

Carbon and cost accounting for liner shipping under the European Union Emission Trading System

Excessive CO2 emissions and increased total costs of liner shipping are the two main problems affecting the environmental and economic benefits of liner companies under the European Union Emission Trading System (EU ETS). To address the upcoming EU ETS, we propose a carbon and cost accounting model for liner shipping that accurately calculates CO2 emissions and total cost of liner shipping. We conduct a case study that a containership operates on the liner route from the Far East to Northwest Europe. The results show that the sailing stage plays a pivotal role in CO2 emissions from liner shipping, accounting for 94.70% of CO2 emissions. Among four types of fuel, CO2 emissions from liner shipping using MGO is the largest, while CO2 emissions from liner shipping using methanol is the smallest. Methanol, as an alternative fuel, proves to be a better choice than LNG for CO2 control of liner shipping. The relationship between sailing speed and CO2 emissions follows a U-shaped curve for the selected containership. Notably, speed reduction is effective in carbon control of liner shipping only when the sailing speed exceeds 8.29 knots. Under the EU ETS, sailing speed is a key variable affecting the total cost of liner shipping. Speed reduction may not always be cost-effective. When keeping the total cost of liner shipping unchanged, sailing speed should be reduced as the EU allowance (EUA) price rises within a certain range. For the selected containership using MGO and HFO, the most economical sailing speed is 8.29 knots, corresponding to the increase in EUA price of 304.95% and 261.21%, respectively. If EUA price continues to rise, speed reduction will become ineffective in controlling the total cost of liner shipping. This model can enhance the environmental and economic benefits of liner companies, meet compliance requirements of the EU ETS, and provide a new perspective for carbon and cost control of liner shipping.

Interpretable EU ETS Phase 4 prices forecasting based on deep generative data augmentation approach

This paper proposes an interpretable deep learning method based on generative data augmentation for forecasting carbon allowance prices in the EU Emissions Trading System (ETS) Phase 4. Utilizing TimeGAN, we generate near-real expanded data to enhance the training sets. Temporal Fusion Transformer (TFT) is used to quantify the contribution of impact factors. The results show that the augmentation effectively improved the prediction accuracy. Interpretability analysis reveals that Brent crude oil, NBP natural gas, and Rotterdam coal are the top three contributors. Our findings offer a strong approach for the new phase price forecasting, helping market participants and policymakers in informed decision-making.

Heat Pump as an Emission Reduction Measure for Ships

Greenhouse gas regulations from the International Maritime Organization, such as the Carbon Intensity Indicator and the Energy Efficiency Existing Ship Index are drawing attention to the implementation of energy efficiency technologies in ships to lower emissions. Presently, more attention is paid to energy efficiency measures related to propulsion (e.g. speed management) and auxiliary energy use (e.g. onshore power). This study compares the environmental impact and cost of replacing heat pumps as an energy efficiency measure instead of oil-fired boilers for two case study vessels by comparing the life cycle impact of different strategies to fulfill the thermal load of vessels while at the port. In terms of life cycle emissions, the heat pump operated using onshore power has the potential to reduce global warming potential by 88% compared to an oil-fired boiler. This accounts for saving 3% and 8% of annual greenhouse gas emissions from entire ship operations, including emissions from engines for the respective case study ships. In addition, shifting to a heat pump avoids NOx and SOx emissions, which adversely affect air quality in the populated areas near the port. Cost results show that the heat pump has an overall higher cost of ownership for case study vessel 1 and a lower cost of ownership for case study vessel 2 compared to oil-fired boiler. Depending on the energy use of specific ships, heat pumps can be cost-competitive at existing carbon emission allowance prices (approximately 90€/tCO2) in the European emission trading system. For the assessed cases, with the emission trading scheme, the return on investment is less than six years and three years for case study vessels 1 and 2 respectively. The study also shows that operating a heat pump is more cost-effective than directly using electro-fuel in a boiler for thermal loads.

Carbon and financial performance nexus of the heavily polluting companies in the context of resource management during COVID-19 period

The depletion of fossil fuels and the new EU climate policy goals to achieve a zero-emission economy after 2050 encourage highly polluting companies to increase the efficiency of energy conversion from non-renewable energy sources, develop renewable energy sources and implement resource management systems in production. Studying the relationship between environmental performance and financial performance of firms is particularly important in times of the Covid-19 pandemic, as companies' decisions to limit or continue green investments and low-emission transformation may impact access to external financing sources. The aim of this study is to identify the shape of the relationship between the carbon and financial results of heavily polluting enterprises in the context of resource management and to assess the impact of the Covid-19 pandemic on the considered relationship. For a panel sample of Polish enterprises representing highly polluting industries with manufacturing plants covered by the EU ETS in 2009–2021, a third-degree polynomial describing this relationship was estimated. A U-shaped relationship was identified between carbon emission intensity and return on sales and return on assets, which can be explained by the ‘too-little-of-a-good-thing’ effect. Increase in share of renewable energy in the production structure significantly reduces firms’ profitability, which can be explained by the relatively high costs associated with the development of renewable energy and the changing legal regulations regarding this type of investment. It has been shown that companies with high carbon emission intensity achieved lower sales profits and return on assets compared to companies with lower carbon intensity during the Covid-19 pandemic. The lower scale of allocation of free emission allowances compared to previous years, combined with the increase in emission allowance prices under the influence of shocks generated by the pandemic, had a negative impact on the profitability of heavily polluting companies. The modelling results provide important information to managers who, based on them, can make more informed decisions regarding the use of natural resources in production and the involvement of enterprises in investments aimed at the use of cleaner production technologies and the development of renewable energy.

Climate actions, market beliefs, and monetary policy

This paper studies the role of expectations and monetary policy in the economy's response to climate actions. We show that in a stochastic environment, without the standard assumption of the perfect rationality of agents, there is more uncertainty regarding the path and the economic impact of a climate policy, with a potential threat to the ability of central banks to maintain price stability. Market beliefs and behavioral agents increase the trade-offs inherent to the chosen mitigation tool, with a carbon tax entailing more emissions uncertainty than in a rational expectations model and a cap-and-trade scheme implying a more pronounced pressure on allowance prices and inflation. The impact on price stability is worsened by delays in the implementation of stringent climate policies, the lack of confidence in the ability of central banks to keep inflation under control, and the adoption of monetary rules tied to expectations rather than current macroeconomic conditions. Central banks can implement successful stabilization policies that reduce the uncertainty surrounding the impact of climate actions and support the greening process while remaining within their mandate.

Modernization Potential of Pulverized Coal Boilers in Response to Contemporary Ecological Challenges

The paper presents an analysis of the possibility of modernization of pulverized coal (PC) boilers fired with hard coal or lignite in order to meet contemporary ecological challenges. The increase in the price of CO2 emission allowances causes a decrease in the profitability of coal combustion. Therefore, the construction of large new coal-fired units is becoming more and more risky. The need to keep existing power plants in operation gives a strong impetus to retrofitting PC boilers to reduce their carbon footprint. The paper assesses the following modernizations in terms of improving the environmental impact of the power plant: increasing the efficiency of the boiler, maintaining high efficiency in a wide range of loads, enabling continuous operation with the lowest load, and increasing the unit load change (ramp rate).

Evaluation and quantitative characterization for the ecological environment impact of open pit mining on vegetation destruction from landsat time series: A case study of Wulishan limestone mine

Quantitatively evaluating the ecological environment impacts of vegetation destruction due to open-pit mining activities is vital for enhancing the green mining standard and cost management capabilities of mining enterprises. Based on the Landsat time series, this study proposes an ecological environment impact assessment and quantitative characterization method for vegetation destruction in mining areas resulting from open-pit mining activities. First, the modified normalized difference water index and the normalized difference vegetation index time series data were calculated. The water body thresholds and the fraction of vegetation coverage were ascertained using the K-means clustering algorithm and the dimidiate pixel model, respectively, to determine the area of direct vegetation destruction in mining areas. Second, utilizing the Theil-Sen Median trend analysis and the Mann-Kendall test, the indirect impact area of vegetation in the mining region was identified. Lastly, by integrating vegetation's net primary productivity with the Chinese Emission Allowance price index, the total carbon emission cost of vegetation destruction due to mining activities over 20 years was calculated to be about 2.122 million yuan. The findings indicated that the ecological environmental impact of open-pit mining activities on vegetation destruction cannot be ignored. From 2000 to 2020, open-pit mining at the Wulishan limestone mine in Anhui Province, China, increased the area of direct vegetation destruction by 9.072 × 105 m2, and the indirect impact area on vegetation was 7.371 × 105 m2. The carbon emission cost of vegetation destruction in the direct destruction area was about 104,000 yuan per year, and the carbon emission cost of vegetation damage in the indirect impact area was approximately 2,082.53 yuan per year. This research provides a scientific foundation for ecological environmental protection, regulations, green mining, and cost management for mining enterprises, promoting the harmonious progress of both the economy and environmental protection.