Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

The carbon market is recognized as the most effective means for reducing global carbon dioxide emissions. Effective carbon price forecasting can help the carbon market to solve environmental problems at a lower economic cost. However, the existing studies focus on the carbon premium explanation from the perspective of return and volatility spillover under the framework of the mean-variance low-order moment. Specifically, the time-varying, high-order moment shock of market asymmetry and extreme policies on carbon price have been ignored. The innovation of this paper is constructing a new hybrid model, NAGARCHSK-GRU, that is consistent with the special characteristics of the carbon market. In the proposed model, the NAGARCHSK model is designed to extract the time-varying, high-order moment parameter characteristics of carbon price, and the multilayer GRU model is used to train the obtained time-varying parameter and improve the forecasting accuracy. The results conclude that the NAGARCHSK-GRU model has better accuracy and robustness for forecasting carbon price. Moreover, the long-term forecasting performance has been proved. This conclusion proves the rationality of incorporating the time-varying impact of asymmetric information and extreme factors into the forecasting model, and contributes to a powerful reference for investors to formulate investment strategies and assist a reduction in carbon emissions.

Integrated Carbon Policy Design for Achieving Net-Zero Targets

Several countries have set net-zero targets, and many more will announce in the next few years. Countries have used carbon pricing as an instrument to cut Greenhouse Gas (GHG) emissions and provide a price signal to attract private investments to achieve net-zero targets. However, current carbon policy in countries with net-zero targets remains inadequate and asymmetrical to overcome net-zero challenges; there are visible gaps in the carbon price level, sectoral coverage, and mechanism to reward carbon-neutral initiatives. This paper proposed an integrated carbon policy design covering economic, technical, and social dimensions and discussed how an integrated policy design approach could be effective in helping countries achieve net-zero objectives. The paper makes recommendations for net-zero policymakers. First, a stable and appropriate carbon price must be in place to attract private investments in carbon offset measures and commercialize clean technologies. Second, governments should use an effective revenue recycling mechanism to engage firms and citizens in mitigating the side effects of the carbon price regime and win their trust. Third, countries should promote behavioral changes and carbon footprint reduction measures through citizen participation. 

Oil Prices, Emission Permits Trade of Carbon, and the Dependence Between Their Quantiles

The relations between carbon and oil market is concerned by many scholars but little research has focused on the dependence between their quantiles. We use Quantile on Quantile Regression method to study the impact of WTI crude oil price and Daqing crude oil price on carbon price and use wavelet analysis to clean and decompose the time series. Results show that the impact of crude oil on carbon is heterogeneous. Research based on the original sequence shows that crude oil price has a positive impact on carbon price at all quantile levels. Research based on decomposition sequence shows that the positive impact of crude oil on carbon begins to weaken, the zero effect begins to increase, and the negative impact also begins to appear. However, the negative impact on carbon price becomes stronger with the stability of the time series data obtained from the decomposition of crude oil price series gradually improving, while the positive impact gradually weakens.

A Novel Carbon Price Fluctuation Trend Prediction Method Based on Complex Network and Classification Algorithm

Carbon price fluctuation is affected by both internal market mechanisms and the heterogeneous environment. Moreover, it is a complex dynamic evolution process. This paper focuses on carbon price fluctuation trend prediction. In order to promote the accuracy of the forecasting model, this paper proposes the idea of integrating network topology information into carbon price data; that is, carbon price data are mapped into a complex network through a visibility graph algorithm, and the network topology information is extracted. The extracted network topology structure information is used to reconstruct the data, which are used to train the model parameters, thus improving the prediction accuracy of the model. Five prediction models are selected as the benchmark model, and the price data of the EU and seven pilot carbon markets in China from June 19, 2014, to October 9, 2020, are chosen as the sample for empirical analysis. The research finds that the integration of network topology information can significantly improve the price trend prediction of the five benchmark models for the EU carbon market. However, there are great differences in the accuracy improvement effects of China’s seven pilot carbon market price forecasts. Moreover, the forecasting accuracy of the four carbon markets (i.e., Guangdong, Chongqing, Tianjin, and Shenzhen) has improved slightly, but the prediction accuracy of the carbon price trend in Beijing, Shanghai, and Hubei has not improved. We analyze the reasons leading to this result and offer suggestions to improve China’s pilot carbon market.

Agri-pellets as alternative fuels for coal-fired power plants in Canada

Purpose Lignite coal-fired power plants are the main electricity generators in the province of Saskatchewan, Canada. Although burning lignite coal to generate power is economical, it produces significant greenhouse gases making it a big challenge to Canada’s international commitment on emission reduction. However, abundant agricultural crops and sawdust produced in Saskatchewan put the province in a good position to produce and use agri-pellets as an alternative fuel to generate electricity. This study aims to conduct an economic and environmental analysis of the replacement of lignite coal by agri-pellets as the fuel for Saskatchewan’s coal-fired power plants. Design/methodology/approach The study estimates the economic and environmental costs and benefits of two alternative fuels for power plants. The economic analysis is based on the pellet production and transportation costs from farms to production sites and from the production sites to power plants. In the production process, biomass precursors are densified with and without additives to produce fuel agri-pellets with appropriate mechanical durability and high heating value per volume unit. The environmental analysis involves estimation of greenhouse gas emissions and their social costs for lignite coal and different types of agri-pellets under different scenarios for pellet production and transportation. Findings The results show that although the total cost of electricity is lower for coal than agri-pellets, the gap shrinks when social costs and specifically a carbon price of $50/tonne are included in the model. The cost of electricity in lignite coal-fired power plants would also be on par with agri-pellets-fired power plants if the carbon price is between U$68 and $78 per tonne depending on the power plant locations. Therefore, a transition from coal to agri-pellet fuels is feasible if a high-enough price is assigned to carbon. The method and the results can be generalized to other places with similar conditions. Research limitations/implications There are a few caveats in this study as follows. First, the fixed costs associated with the transformation of the existing coal-fired power plants to pellet-fired plants are not considered. Second, the technological progress in the transportation sector, which would favor the net benefits of using pellets versus coal, is not included in the analysis. Finally, the study does not address the possible political challenges facing the transition in the context of the Canadian federal system. Practical implications The study results indicate that the current carbon price of $50 per tonne is not sufficient to make the agri-pellets a feasible source of alternative energy in Saskatchewan. However, if carbon pricing continues to rise by $15 annually starting in 2022, as announced, a transition from coal to agri-pellets will be economically feasible. Social implications Canada is committed to reduce its emission according to the Paris agreement, and therefore, needs to have a concrete policy to find alternative energy sources for its coal-fired power plants. This study examines the challenges and benefits of such transition using the existing agri-pellet resources in Saskatchewan, a province with abundant agricultural residues and coal-fired power plants. The findings indicate that a significant emission reduction can be achieved by using agri-pellets instead of coal to produce electricity. The study also implies that the transition to renewable energy is economical when social costs of carbon (carbon tax) is included in the analysis. Originality/value As far as the authors know, this is the first study providing a socio-economic analysis for a possible transition from the coal-fired power plants to a more clean and sustainable renewable energy source in one of the highest carbon dioxide (CO2) producer provinces in Canada: Saskatchewan. The study builds upon the technical production of three agri-pellets (oat hull, canola hull and sawdust) and estimates the economic and environmental costs of alternative fuels under different scenarios.

Effects of demand-side restrictions on high-deforestation palm oil in Europe on deforestation and emissions in Indonesia

Demand-side restrictions on high-deforestation commodities are expanding as a climate policy, but their impact on reducing tropical deforestation and emissions has yet to be quantified. Here we model the effects of demand-side restrictions on high-deforestation palm oil in Europe on deforestation and emissions in Indonesia. We do so by integrating a model of global trade with a spatially explicit model of land-use change in Indonesia. We estimate a European ban on high-deforestation palm oil from 2000–2015 would have led to a 8.9% global price premium on low-deforestation palm oil, resulting in 21,374 ha/yr (1.60%) less deforestation and 21.1 million tCO2/yr (1.91%) less emissions from deforestation in Indonesia relative to what occurred. A hypothetical Indonesia-wide carbon price would have achieved equivalent emission reductions at $0.81/tCO2. Impacts of a ban are small because: 52% of Europe’s imports of high-deforestation palm oil would have shifted to non-participating countries; the price elasticity of supply of high-deforestation oil palm cropland is small (0.13); and conversion to oil palm was responsible for only 32% of deforestation in Indonesia. If demand-side restrictions succeed in substantially reducing deforestation, it is likely to be through non-price pathways.

A VaR-Based Methodology for Assessing Carbon Price Risk across European Union Economic Sectors

The latest European Union measures for combating climate adopted in the “Fit for 55 package” envisage the extension of the Emissions Trading System, the first “cap-and-trade” system in the world created for achieving climate targets, which limits the amount of greenhouse gas emissions by imposing a price on carbon. In this context, our study provides an integrated assessment of carbon price risk exposure of all economic sectors in the European Union Member States, thus supporting decision making in determining the energy transition risk. We propose a novel approach in assessing carbon risk exposure using the Value at Risk methodology to compute the carbon price under the EU ETS, based on historical price simulation for January–August 2021 and ARMA-GARCH models for the October 2012–August 2021 period. We further built a value erosion metric, which allowed us to establish each sector’s exposure to risk and to identify differences between Eastern and Western EU countries. We find that the refining sector appears to be highly vulnerable, whereas there is higher potential for large losses in the energy supply and chemical sectors in Eastern EU Member States, given a different pace of industry restructuring.

Marginal Abatement Cost of Carbon Emissions under Different Shared Socioeconomic Pathways

Future emissions scenarios have served as a primary basis for assessing climate change and formulating climate policies. To explore the impact of uncertainty in future emissions scenarios on major outcomes related to climate change, this study examines the marginal abatement cost (MAC) of carbon emissions under the latest Shared Socioeconomic Pathways (SSPs) subject to the economic optimum and the 1.5 °C temperature increase constraint using the Epstein-Zin (EZ) climate model. Taking the ”Regional Rivalry” (SSP3) scenario narrative under the economic optimum as a representative case, the expected MACs per ton CO2 equivalent (CO2e) emissions in the years 2015, 2030, 2060, 2100, and 2200 are: $102.08, $84.42, $61.19, $10.71, and $0.12, respectively. In parallel, the associated expected average mitigation rates (AMRs) are 0%, 63%, 66%, 81%, and 96%, respectively. In summary, in a world developing towards regional rivalry (SSP3) or fossil-fueled development (SSP5) with high mitigation pressure, the MAC values have approximately doubled, compared with the sustainability (SSP1) and inequality (SSP4) storylines with low mitigation pressure levels. The SSP2 (Middle of the Road) shows a moderate MAC decreasing trend with moderate mitigation pressure. The results provide a carbon price benchmark for policy makers with different attitudes towards the unknown future and can be used to formulate carbon mitigation strategy to respond to specific climate goals.