Carbon Price Research Articles

Carbon pricing acceptance – the role of revenue recycling among households and companies in Norway

ABSTRACT Effective carbon taxation is essential for achieving the green transition. However, there is typically stiff opposition to carbon taxation due to adverse equity and other impacts. Hence, it is essential to get a better understanding of which factors, including the use of tax revenue, can increase acceptability. To date, stated preference methods, rarely used to analyse this issue, have focused only on households. We conduct two national choice experiment surveys of Norwegian households and companies. The experiments include carbon taxes on transport fuel and associated emission reductions and different revenue recycling options as attributes. We find that both groups are more accepting of higher tax levels if the revenues finance climate change mitigation efforts. There is some heterogeneity among the groups with regard to using revenues to reduce different dimensions of inequality. Simulating policy scenarios, we find acceptance for the highest carbon tax among both groups when the revenues are used both to finance climate change mitigation efforts and to reduce rural-urban inequalities. This policy option points to an acceptable carbon tax close to an estimated level necessary for reaching the most ambitious climate target set by the Norwegian government. In Norway, further research should explore whether an effective carbon tax level could be achieved with modest efficiency loss, while also alleviating inequality.

Carbon management technology pathways for reaching a U.S. Economy-Wide net-Zero emissions goal

The Carbon Management Study Group of the 37th Energy Modeling Forum (EMF 37) designed seven scenarios to explore the role of three potentially key technology suites – point source carbon dioxide capture and storage (PSCCS), direct air capture of carbon dioxide (DACCS), and hydrogen systems (H2) – in shaping the broader technology pathways to reaching net-zero carbon dioxide (CO2) emissions in United States by 2050. Each scenario was run by up to 13 models participating in the EMF 37 study. Results show that carbon dioxide removal technologies were consistently a major part of successful pathways to net-zero U.S. CO2 emissions in 2050. Achieving this net-zero CO2 goal without any form of carbon dioxide capture and storage was found to be impossible for most models; some models also found it impossible to reach net-zero without DACCS. The marginal cost of achieving net-zero CO2 emissions in 2050 was between two and 10 times higher without PSCCS and/or DACCS available. The carbon price at which DACCS was deployed as a backstop technology depended upon the assumed cost at which DACCS was available at scale. Carbon prices were between $250 and $500 per ton CO2 when DACCS deployed as a backstop. The average CO2 capture rate across all models in 2050 in the central net-zero scenario was 1.3 GtCO2/year, which implies a substantial upscaling of capacity to move and store CO2. Hydrogen sensitivity scenarios showed that H2 typically constituted a relatively small share of the overall U.S. energy system; however, H2 deployed in applications that are considered hard to decarbonize, facilitating transition towards net-zero emissions.

Which decarbonization policy mixes are better for China's power sector? A simulation balancing aggregate abatement effects and economic impacts

The decarbonization policies are currently being planned or implemented worldwide, particularly for emerging economies such as China. When the government designs a preferred decarbonization policy mix, both environmental and economic impacts should be considered. This paper focuses on decarbonization policy mixes in China's power sector, including renewable electricity subsidies (ES), tradable green certificates (TGC), and two carbon pricing instruments. An integrated model combining a partial equilibrium model and an input-output framework was constructed to investigate their abatement effects and economic impacts. The results show that the combination of multiple decarbonization policies can achieve greater emission reduction or lower economic losses compared to implementing them separately. The mix of carbon pricing and TGC shows the least economic losses; the mix of carbon pricing, ES, and TGC achieves maximum abatement effect. A narrow focus on carbon emission reduction would hinder the economic development. The combination of multiple decarbonization policies can trigger more substantial variations in the supply chain of the power sector than their individual implementation. Those policy mixes including carbon tax may achieve lower carbon emissions and economic losses compared to those including a carbon emissions trading market. Therefore, policymakers should conduct a comprehensive simulation of the multiple impacts of policy mixes before implementing them, encompassing both direct impacts on the power sector and indirect impacts on its upstream sectors.

Greenflation, a myth or fact? Empirical evidence from 26 OECD countries

Among the various effects of the green transition on the macroeconomy, we examined the unintended effects of the green transition on inflation, the so-called “greenflation”. We analyzed panel data from 26 OECD countries covering the period from 2005 to 2019 using four models based on the New Keynesian Phillips curve model. To date, only a few studies have examined greenflation, particularly in terms of empirical investigation. The main contribution of this study to the literature is to provide empirical evidence of greenflation. We also provide empirical evidence on the issues in the NKPC literature that the output gap better explains inflation dynamics than unit labor costs and confirm the demise of the Phillips curve. Estimates from the Common Correlated Effect Mean Group (CCEMG), Augmented Mean Group (AMG), and Panel Corrected Standard Error (PCSE) models indicate that the green transition has an upward inflationary impact on headline inflation, but no significant impact on core inflation. In addition, we have examined four circumstances under which the green transition may affect inflation that have not been sufficiently discussed in the literature: increased green transition spending, carbon pricing, increased demand for critical minerals, and the low energy density of renewable energy. Each of these circumstances has the potential to exert both upward and downward pressures simultaneously. Our suggestion to policymakers is that they do not yet need to change their current view of monetary policy with respect to greenflation, but they should carefully consider policy designs that take into account the unintended effects of the energy transition.

The Contagion of Debt Default Risk in Energy Enterprises Considering Carbon Price Fluctuations

Under the constraints of low-carbon transformation goals, energy enterprises have significantly increased their debt default risk levels due to carbon price fluctuations. This article first analyzes the contagion mechanism of debt default risk among energy enterprises, and based on this, constructs a debt default risk contagion model among energy enterprises considering carbon price fluctuations, and then simulates and analyzes the evolution characteristics of debt default risk contagion among energy enterprises. The research results indicate that: (1) As the proportion of carbon emission cost increment and investor sentiment index increase, the stability of the debt network of energy enterprises strengthens. As the ratio of commercial credit among energy enterprises and influence of energy enterprises increase, the impact of debt risk gradually intensifies. (2) The investor sentiment index has a strengthening effect on the influence of energy enterprises, the proportion of commercial credit among energy enterprises, and the proportion of carbon emission cost increment. The commercial credit ratio between energy enterprises and its influence has a mutually reinforcing effect. (3) The investor sentiment index has suppressed debt default risk for various energy enterprises. The joint risk suppression effect of the proportion of carbon emission cost increment and the influence of energy enterprises in petroleum and petrochemical enterprises is more prominent. The joint risk constraint ability between the proportion of carbon emission cost increment and investor sentiment index in coal enterprises is stronger.

Cost estimation of Non-CO2 greenhouse gas emissions reduction- A bottom-up analysis of coal-bed methane extraction and utilization in Shanxi, China

This paper analyzes the synergistic effect of coal-bed methane (CBM) extraction and utilization on greenhouse gas (GHG) emissions reduction in Shanxi Province. Using data from a survey of 87 CBM extraction firms and 61 CBM utilization firms, we draw abatement cost curves of methane emission reduction with the "Conserved Supply Curve (CSC)." Our findings show large diversity among the methane abatement costs of extraction firms, meanwhile cost diversity in the extraction step is mainly due to differences in extraction technologies, rather than mine types. Power generation is the main utilization option for CBM, with much smaller abatement cost diversity than in the extraction step. Most utilization projects are established near CBM sources, with small-scale equipment supplied by domestic vendors. We suggest Shanxi could introduce a carbon pricing mechanism to further encourage CBM extraction and increase its scale. Promoting technology standardization can reduce cost diversity among extraction firms, while developing CBM purifying firms can create standardized products and break the strong integration of extraction and utilization. Strengthening CBM transportation infrastructure can form a competitive market with multi-source supply upstream and efficient transportation downstream. Promoting joint research and development for domestic CBM utilization equipment can also improve its scale.

The social cost of carbon falling on the wealthy

Under the Paris Agreement, the responsibility for reducing greenhouse gas emissions lies with individual countries, which submit pledges to the United Nations Framework Convention on Climate Change (UNFCCC) outlining their ambitions and timelines for emission reductions. Within countries, emissions are unevenly distributed among individuals, with the wealthy known to emit disproportionately more. Since greenhouse gas emissions represent a social cost, this paper investigates the economic implications of the lifestyles of the wealthy. The social cost of carbon (SCC) attributed to millionaires—defined based on their assets, including property—is calculated using national emissions data, distributional data, and SCC assessments for both historical (1990–2022) and future periods (2023–2050). The results suggest that only 1.3% of the global SCC, estimated at US$8.7 trillion, was covered by any form of carbon pricing in 2022. Of this amount, 16.5% (US$1.4 trillion) is attributable to millionaires, who represent just 0.8% of the world's population. The most cost-effective climate policy would be to internalize the SCC, ensuring that the carbon price is equal to the SCC. If the SCC remains externalized, the wealthiest individuals could benefit by an estimated US$25.1 trillion over the period from 2023 to 2050. Other market-based mechanisms will be necessary to curb the carbon-intensive consumption patterns of the wealthy.

Carbon Price Forecasting Based on Influencing Factor Screening and VMD-BIGRU Hybrid Model: A Case of Hubei Carbon Market in China

Carbon Price Forecasting Based on Influencing Factor Screening and VMD-BIGRU Hybrid Model: A Case of Hubei Carbon Market in China

Analysis of the influencing factors of emission trading price in China

China established pilot carbon markets in 2013. In 2020, it set targets for carbon peaking in 2030 and carbon neutrality by 2050. China’s national carbon market officially commenced operations in 2021. Based on the national market and seven pilot markets, this study established the factors influencing carbon trading prices by examining market participants, macroeconomics, energy prices, carbon prices in other markets, etc. Asymmetrical development among the seven pilot cities, for which the study employed a mixed-effects model, was the primary factor impacting carbon prices. The carbon prices in the pilot cities cannot be extrapolated to the entire country. In the national carbon market, where the study employed a multiple regression lag model, the SSE index was positively correlated with carbon prices, whereas the Dow Jones index had no significant effect on carbon prices in terms of macroeconomics. Coal and natural gas prices were negatively correlated with carbon prices, whereas oil prices were positively correlated with energy prices. The EU market prices have a positive correlation with prices in other markets. The significance of this study is that it covers the largest national Emissions Trading System (ETS) in the world and allows for comparing the characteristics of the Chinese market with those of other ETS markets. Additional studies, including more sectors, should be conducted as China’s ETS coverage increases.

Does Speculation Matter in the Carbon Pricing Framework? Insights from the EU Emissions Trading System

To align with the global goal of keeping temperature below [Formula: see text]C, a market-based initiative, “Emissions Trading System” (ETS), has been developed to mitigate climate change. However, while the carbon allowances traded at the ETS are mostly held and traded by polluting companies, financial actors engage in “speculation”, activities that might be detrimental to the functioning of the ETS have also invested in the ETS. By drawing from the big data archive of Google Trends, we construct a news-based speculation index to proxy for the role of speculation in the dynamics of carbon pricing. Given our preliminary finding of inherent volatility and the mixed-frequency nature of the dataset, we employ the GARCH-MIDAS econometric technique to test the hypothesis that an all-inclusive framework that reflects the emission compliance and emissions non-compliance dynamics of the ETS is the most accurate approach to modeling carbon prices. We show that higher speculation in the ETS fosters higher long-term volatility in carbon prices, that speculation is a good predictor of carbon prices, and that its positive impact on carbon price returns makes the ETS an attractive investment opportunity. We provide a data-driven framework upon which the growing debate about whether the behavior of the non-compliance emission actors in the ETS endangers or benefits the functioning of the ETS can be evaluated empirically.

Designing green logistics networks under carbon tax policy: Post-COVID condition

This paper proposes a fuzzy-based method for estimating demand in forward and reverse logistics networks (F&RLNs) subject to carbon pricing in the post-COVID era. We investigate the complex relationships between carbon prices, the reverse logistics network (RLN), and the ever-changing product demand by utilizing fuzzy inference systems (FIS) capabilities. The case study from Mexico confirms the effectiveness of the proposed technique. The findings demonstrate the precision and predictive power of our proposed FIS-based method and show how well it can predict the number of items in demand and the number of goods that will be returned and subject to carbon taxation in the post-COVID era. The results illustrate the significant impact that carbon pricing has on the RLN and the associated product demand. For logistics managers seeking to make informed decisions about the establishment and operation of forward and RLNs within the carbon pricing paradigm, this empirical data provides insightful information in the post-COVID era.

How effective is the carbon pricing implementation in ASEAN Member States to reduce regional emissions

Abstract At the Conference of the Parties of the UNFCCC (COP 27), the ASEAN Member States (AMS) reaffirmed their commitment to the Paris Agreement by pledging to achieve net zero or carbon neutrality targets by 2050. According to the 7th ASEAN Energy Outlook (AEO7) study, ASEAN’s GHG emission will rise 3.7-fold to 6,704 MtCO2-eq in 2050 from 1,815 Mt in 2020. This will require all AMS to scale up their efforts to reach their greenhouse gas (GHG) emission target, and one of them is by introducing carbon pricing to smoothen the transition by encouraging more renewable energy use. Currently, some AMS have already introduced or proposed carbon pricing. Singapore has introduced carbon tax implementation since 2019. Indonesia has been stepping up its commitment to enforce both carbon tax and emission trading system (ETS) by introducing numerous supporting policies. Malaysia, Thailand, and Vietnam are anticipating launching ETS as their latest climate action plan. A question on how effective carbon pricing implementation to reduce regional emission may come up and this paper shed lights to answer this question through expert interviews and literature reviews. The findings indicate that carbon pricing may reduce emissions, especially from power generation and manufacturing industry, and support the transition to a low-carbon economy, helping the region reach carbon neutrality or net zero targets by 2050.

Investigating the Impacts of Carbon Pricing Mechanism on CCS development in ASEAN countries

Abstract Carbon Capture and Storage (CCS) is a technology that contributes toward a reduction in carbon dioxide (CO2) emissions from large point sources. It has been proven to be a crucial technology to decarbonise ASEAN’s hard-to-abate industry sector. ASEAN countries are a concentrated cluster, contributing to significant emissions, and provides the potential for suitable geological storage for CCS according to current studies. This paper discusses the impact of carbon pricing on the CCS development in ASEAN countries, through studying factors that enable or disable successful CCS projects. Causal Loop Diagram (CLD), as a part of system dynamics, was employed as an analytical tool to identify and visualize the key variables, system’s structure, the network of existing carbon pricing mechanisms and other financing instruments needed in CCS deployment. The results of this study indicate that strong public and private finance, the development of carbon markets, and carbon pricing policies are the key enablers for CCS projects in ASEAN. Carbon pricing should be viewed as an effective catalyst that helps the growth of CCS with the right policies in place, government and regulatory support, and market forces.

A hybrid carbon price forecasting model combining time series clustering and data augmentation

Stable and accurate carbon price forecasts are crucial for carbon asset management and policy design. Therefore, various advanced models have been proposed to forecast carbon price, and decomposition–reconstruction models occupy a major position. On this basis, this study proposes an improved decomposition–reconstruction framework and constructs a novel hybrid forecasting model Informer-DA-BOHB-TVFEMD-CL. The hybrid model decomposes carbon price by TVFEMD to obtain different volatility characteristics and classifies these features by time series clustering, where the high-frequency components are retained and the low-frequency components are reconstructed. Then, the training set was expanded by the generative data augment technique to improve the model's generalization ability and avoid overfitting. Finally, the advanced deep learning model Informer and the hyperparameter optimization technique BOHB are used to model the training set, and the prediction results of each subcomponent are integrated to obtain the final prediction results. Experiments are conducted in EU-ETS, Guangdong, Hubei, and Chinese national carbon market. Experimental results show that the stability and prediction performance of the proposed hybrid model are better than those of the benchmark models, proving the applicability of the improved decomposition–reconstruction idea and the validity of the hybrid model. Meanwhile, the proposed model has demonstrated good profitability in carbon trading.

Holistic life cycle cost analysis of road bridges with non-metallic reinforcement

Corrosion-related damage due to exposure to environmental conditions is the main cause of costs in the maintenance of transport infrastructure. Because of its high corrosion resistance and the associated higher durability, non-metallic reinforcement offers great potential for preventing such damage, thus reducing maintenance costs. In this article, potential savings for an integral road bridge are studied through a holistic life cycle cost (LCC) analysis considering four different reinforcement materials (steel, glass, basalt, carbon). A fair comparison is enabled by a material-specific design, the calculation of maintenance and user costs and the consideration of the respective disposal scenario. Moreover, environmental costs are recognised by carbon pricing based on life cycle analysis (LCA). The influence of individual parameters is quantified by means of a sensitivity analysis and the probability of savings is studied by Monte Carlo simulation. It is shown that higher investment costs for non-metallic reinforcement can be compensated by lower user costs. This is mainly due to shorter maintenance periods, as less time is required for repair action, whereby potential savings in user costs are particularly evident if the traffic route below the bridge is not disrupted. It is concluded that even from today's perspective, the use of glass and basalt fibre-reinforced polymer (FRP) reinforcement in highway bridges with average traffic volumes very likely offers an economic advantage over corrosion-prone reinforcing steel.

Carbon Border Adjustment Mechanism (CBAM) Implementation on Reducing Emission in the ASEAN Energy Sector

Abstract In the context of global efforts to combat climate change and transition towards a low-carbon economy, the European Union (EU) has introduced the Carbon Border Adjustment Mechanism (CBAM) as part of its ambitious Green Deal. This mechanism aims to address carbon leakage from high-emitting industries and ensure fair conditions for European industries. The ASEAN region, as highlighted in the 7th ASEAN Energy Outlook (AEO7) study, is projected to experience a substantial increase in greenhouse gas (GHG) emissions, reaching 6,704 Mt CO2-eq by 2050 compared to a baseline scenario of 1,815 Mt CO2-eq in 2020. Consequently, ASEAN has recognized the urgency to earnestly reduce its emissions in response to the global push for carbon neutrality. The implementation of CBAM by the EU has prompted many countries, including ASEAN Member States (AMS) exporting goods to the EU and beyond, to consider the adoption of CBAM policies. This study examines the potential implications of CBAM for the emission reduction in high-emitting industries in ASEAN (such as cement, iron and steel, aluminum, fertilizers, and electricity generation). The findings indicate that the implementation of CBAM may result in higher product prices for these industries, as they will be required to account for the climate footprint of their manufacturing activities through additional fees. However, it also presents an opportunity for emission reductions within these industries by incentivizing the adoption of renewable energy sources to mitigate their carbon footprint during manufacturing processes. To effectively respond to the implementation of CBAM, the region should adopt three key strategies: 1) active participation in and implementation of carbon pricing mechanisms, 2) increased investments in low carbon technologies, and 3) establishment of strategic partnerships to enhance trade relations between the EU and ASEAN.

Assessing the Sectoral Economics Consequences of Carbon Price Using Price-Output Model Analysis

Abstract A carbon pricing policy is featured in the Twelfth Malaysia Plan as a considered measure for Malaysia to achieve its net-zero emissions goal by 2050.This study utilised price and output model analysis using Eora Multi-Region Input-Output Table 2016 (MRIO) database to assess the impact of carbon tax policy on price and output changes. The study reveals that the manufacturing sector is the most sensitive to the introduction of a carbon tax, with pronounced effects including a price escalation of 0.54% and a reduction in output of 0.81%. The ranks of sensitivity then followed by agriculture, services, mining and construction sector. the services sector presents a compelling opportunity for policymakers, offering a robust tax revenue stream while displaying lower vulnerability to the tax’s effects. This observation underscores the importance of a strategic approach in carbon tax policy, where the potential for revenue generation is balanced against the differential impacts on sectoral vulnerability. This study provides an insightful information for policymakers to develop strategic plans to ensure a smooth transition towards carbon pricing policy and foster sustainable economic development.

Carbon Price Forecasting Using Optimized Sliding Window Empirical Wavelet Transform and Gated Recurrent Unit Network to Mitigate Data Leakage

Precise forecasts of carbon prices are crucial for reducing greenhouse gas emissions and promoting sustainable, low-carbon development. To mitigate noise interference in carbon price data, hybrid models integrating data decomposition techniques are commonly utilized. However, it has been observed that the improper utilization of data decomposition techniques can lead to data leakage, thereby invalidating the model’s practical applicability. This study introduces a leakage-free hybrid model for carbon price forecasting based on the sliding window empirical wavelet transform (SWEWT) algorithm and the gated recurrent unit (GRU) network. First, the carbon price data are sampled using a sliding window approach and then decomposed into more stable and regular subcomponents through the EWT algorithm. By exclusively employing the data from the end of the window as input, the proposed method can effectively mitigate the risk of data leakage. Subsequently, the input data are passed into a multi-layer GRU model to extract patterns and features from the carbon price data. Finally, the optimized hybrid model is obtained by iteratively optimizing the hyperparameters of the model using the tree-structured Parzen estimator (TPE) algorithm, and the final prediction results are generated by the model. When used to forecast the closing price of the Guangdong Carbon Emission Allowance (GDEA) for the last nine years, the proposed hybrid model achieves outstanding performance with an R2 value of 0.969, significantly outperforming other structural variants. Furthermore, comparative experiments from various perspectives have validated the model’s structural rationality, practical applicability, and generalization capability, confirming that the proposed framework is a reliable choice for carbon price forecasting.

Framework for multivariate carbon price forecasting: A novel hybrid model

The complex characteristics of volatility and non-linearity of carbon price pose a serious challenge to accurately predict carbon price. Therefore, this study proposes a new hybrid model for multivariate carbon price forecasting, including feature selection, deep learning, intelligent optimization algorithms, model combination and evaluation indicators. First, this study collects and organizes the historical carbon price series of Hubei and Shanghai as well as the influencing factors in five dimensions including structured and unstructured data, totaling twenty variables. Second, data dimensionality reduction is performed and input variables are obtained using the least absolute shrinkage and selection operator, followed by the introduction of nine advanced deep learning models to predict carbon price and compare the prediction effects. Then, through the combination of models, three models with the best performance are combined with Pelican optimization algorithm to construct a hybrid forecasting model. Finally, the experimental results show that the developed forecasting model outperforms other comparation models in terms of prediction accuracy, stability and statistical hypothesis testing, and exhibits excellent prediction performance. Furthermore, this study also applies the developed model to European carbon market price prediction and uses the Hubei carbon market as an example for quantitative trading simulation, and the empirical results further verify its robust prediction performance and investment application value. In conclusion, the proposed hybrid prediction model can not only provide high-precision carbon market price prediction for the government and corporate decision makers, but also help investors optimize their trading strategies and improve their returns.

Environmental implications of migration: Worldwide insights

This study investigates the impact of migrants’ skills, employment sectors, urban planning and energy sectors efficiency on carbon emissions. Utilizing the static and dynamic methods Dynamic GMM, Spatial methods on a panel dataset covering 156 nations from 2000 to 2022. Our findings reveal that heightened geopolitical risk leads to both short and long run increases in carbon emissions and the ecological footprint. The results indicate positive impact of international migration, travel services and urbanization on CO2 emissions. The results also investigate interaction effects, revealing the amplifying effect of urban population density on the association between international migration and CO2 emissions. The disaggregated analysis shows that migrants positively impact CO2 emissions in high income, Lower Income while there is complex association in upper middle-income economies. The findings provide policymakers with valuable insights for prioritizing the adoption of renewable energy, sustainable urban planning, energy efficiency measures, sustainable tourism practices, carbon pricing mechanisms, international cooperation, and sustainable economic growth strategies that are tailored to specific country contexts.