The poverty and distributional impacts of carbon pricing on households: evidence from Ghana, Nigeria and Uganda – ERRATUM

Against the backdrop of escalating atmospheric carbon dioxide concentrations, carbon emission trading systems (ETS) have emerged as pivotal policy instruments, with China’s ETS playing a prominent role globally. The carbon price, central to ETS functionality, guides resource allocation and corporate strategies. Due to unexpected events, political conflicts, limited access to data information, and insufficient cognitive levels of market participants, there are epistemic uncertainties in the fluctuations of carbon and energy prices. Existing studies often lack effective handling of these epistemic uncertainties in energy prices and carbon prices. Therefore, the core objective of this study is to reveal the dynamic linkage patterns between energy prices and carbon prices, and to quantify the impact mechanism of epistemic uncertainties on their relationship with the help of uncertain differential equations. Methodologically, a dynamic model of carbon and energy prices was constructed, and analytical solutions were derived and their mathematical properties were analyzed to characterize the linkage between carbon and energy prices. Furthermore, based on the observation data of coal prices in Qinhuangdao Port and national carbon prices, the unknown parameters of the proposed model were estimated, and uncertain hypothesis tests were conducted to verify the rationality of the proposed model. Results showed that the mean squared error of the established model for fitting the linkage relationship between carbon and energy prices was 0.76, with the fitting error controlled within 3.72%. Moreover, the prediction error was 1.88%. Meanwhile, the 5% value at risk (VaR) of the logarithmic return rate of carbon prices was predicted to be −0.0369. The research indicates that this methodology provides a feasible framework for capturing the uncertain interactions in the carbon-energy market. The price linkage mechanism revealed by it helps market participants optimize their risk management strategies and provides more accurate decision-making references for policymakers.

Carbon trading volume and price forecasting in China using multiple machine learning models

This study aims to investigate the co-movement and lead–lag relationship between carbon prices and energy prices in the time–frequency domain in the carbon emission trading system (ETS) of Beijing. Based on wavelet analysis method, this study examines the weekly data on oil and natural gas prices and carbon prices in Beijing ETS from its establishment in November 2013 to April 2019. Empirical results show the following important findings: (1) Carbon and natural gas prices are mainly negatively correlated, with natural gas prices occupying a leading position in the 12–20 weeks frequency band, indicating that the increase (decrease) of natural gas price will lead to the decrease (increase) of carbon price; (2) carbon and oil prices show an unstable dependence relationship, and their leadership position in the market constantly changes. The partial wavelet coherency and partial phase differences vary greatly in different time–frequency domains, indicating that there is no stable coherency between oil prices and carbon prices. The estimation results prove the existence of coherency between the carbon and energy prices in the Beijing ETS. The research findings of this paper provide quantifiable references for investors to achieve risk control in asset allocation and investment portfolio in the ETS market.

The real estate sector must transition towards a low-carbon economy. In current investment decisions, carbon emissions are insufficiently considered and may not contribute to a low-carbon portfolio aligned with the sector's target. Therefore, investors require a change in the current DCF model-based investment decision to direct capital to projects that support this goal. This paper examines the impact of carbon accounting and pricing on a standard investment model using the Discounted Cash Flow (DCF) model. Three additional cash flows are modelled, representing the costs for Embodied Carbon (ECC), Operational Carbon Cost (OCC), and Maintenance Carbon Cost (MCC). This paper introduces a novel application of carbon pricing in real estate investment, accounting for embodied, operational, and maintenance-related emissions during the use phase, which results in a practical framework and guide for practitioners. The Carbon Price needs to be sufficiently high to make an impact and contribute to excluding energy-inefficient assets as an investment opportunity. Furthermore, the influence of ECC is minor compared to OCC, making carbon pricing for ECC less relevant in investment decisions. Ultimately, the MCC is a significant factor to consider when making an investment decision. Carbon pricing can encourage the use of circular and biobased materials, reducing emissions during the construction, renovation, and use phases. Investors should apply a carbon price to affect investment decisions by excluding carbon-intensive assets from investment portfolios. Investors could align their capital with the sector's low-carbon goal by including monetised carbon emissions in an investment decision.

The Case against Carbon Prices

Untangling the entanglement of US monetary policy uncertainty and European natural gas and carbon prices

The scaling up of carbon capture, utilization, and storage (CCUS) deployment is constrained by multiple factors, including technological immaturity, high capital expenditures, and extended investment return periods. The existing research on CCUS investment decisions predominantly centers on coal-fired power plants, with the utilization pathways placing a primary emphasis on storage or enhanced oil recovery (EOR). There is limited research available regarding the chemical utilization of carbon dioxide (CO2). This study develops an options-based analytical model, employing geometric Brownian motion to characterize carbon and oil price uncertainties while incorporating the learning curve effect in carbon capture infrastructure costs. Additionally, revenues from chemical utilization and EOR are integrated into the return model. A case study is conducted on a process producing 100,000 tons of methanol annually via CO2 hydrogenation. Based on numerical simulations, we determine the optimal investment conditions for the “CO2-to-methanol + EOR” collaborative scheme. Parameter sensitivity analyses further evaluate how key variables—carbon pricing, oil market dynamics, targeted subsidies, and the cost of renewable electricity—influence investment timing and feasibility. The results reveal that the following: (1) Carbon pricing plays a pivotal role in influencing investment decisions related to CCUS. A stable and sufficiently high carbon price improves the economic feasibility of CCUS projects. When the initial carbon price reaches 125 CNY/t or higher, refining–chemical integrated plants are incentivized to make immediate investments. (2) Increases in oil prices also encourage CCUS investment decisions by refining–chemical integrated plants, but the effect is weaker than that of carbon prices. The model reveals that when oil prices exceed USD 134 per barrel, the investment trigger is activated, leading to earlier project implementation. (3) EOR subsidy and the initial equipment investment subsidy can promote investment and bring forward the expected exercise time of the option. Immediate investment conditions will be triggered when EOR subsidy reaches CNY 75 per barrel or more, or the subsidy coefficient reaches 0.2 or higher. (4) The levelized cost of electricity (LCOE) from photovoltaic sources is identified as a key determinant of hydrogen production economics. A sustained decline in LCOE—from CNY 0.30/kWh to 0.22/kWh, and further to 0.12/kWh or below—significantly advances the optimal investment window. When LCOE reaches CNY 0.12/kWh, the project achieves economic viability, enabling investment potentially as early as 2025. This study provides guidance and reference cases for CCUS investment decisions integrating EOR and chemical utilization in China’s refining–chemical integrated plants.

This paper provides ex-post empirical evidence on the effects of carbon pricing policies on headline consumer price inflation, as well as on its food, energy and core inflation subcomponents. It considers both carbon taxes and prices of carbon emissions trading systems (ETS) as carbon pricing policies, and a large number of OECD economies from 1995 to 2020. The paper uses dynamic panel estimation of New-Keynesian Phillips curves, which are commonly used for modelling inflation dynamics, and control for macroeconomic variables including the output gap, exchange rate changes and inflation expectations. An increase in prices of ETS by US dollar $10 per ton of carbon dioxide (CO2) equivalents is found to increase energy consumer price inflation by 0.8 percentage points, and headline inflation by 0.08 percentage points, but has no significant effects on food and core consumer price inflation. An increase in carbon taxes by $10 per ton of CO2 equivalents increases food consumer price inflation by 0.1 percentage points, but has no significant effects on energy consumer price inflation, headline and core consumer price inflation. These findings are relevant for future climate policies. They suggest that higher carbon taxes and prices of ETS permits have not led to large increases in headline inflation. Consequently, based on this evidence the use of carbon pricing to speed up the necessary transition to net zero carbon emissions need not be held back by concerns about large overall inflationary effects. This is relevant since these carbon pricing policies have been shown to be effective in reducing carbon dioxide emissions.

In this paper, a multilayer recurrence network is introduced to examine the information linkage between carbon and energy markets. We first construct a multilayer recurrence network of energy and carbon markets, and we define the information linkage coefficient to measure the linkage relationship between the network layers based on the network microstructure. To measure the mutual leading relationship between carbon and energy markets, we construct a time-delay multilayer recurrence network and introduce the time-delay information linkage coefficient to measure the intersystem interaction. The carbon and energy prices, including West Texas Intermediate crude oil, coal, natural gas, and gasoline, from February 22, 2011, to April 1, 2019, are selected as sample data for empirical analysis. The results show that the linkage relationship between oil, coal, natural gas, and carbon prices presents a U-shaped trend in the second, transitional, and third phases of the European Union carbon market, while the linkage trend of gasoline and carbon prices continues to rise. The mutual leading relationship between energy and carbon prices changes in different stages, and carbon price plays a leading role at the present stage.

PurposeThe purpose of this study is to model a vehicle routing problem with integrated picking and delivery under carbon cap and trade policy. This study also provides sensitivity analyses of carbon cap and price to the total cost.Design/methodology/approachA mixed integer linear programming (MILP) model is formulated to model the vehicle routing with integrated order picking and delivery constraints. The model is then solved by using the CPLEX solver. Carbon footprint is estimated by a fuel consumption function that is dependent on two factors, distance and vehicle speed. The model is analyzed by considering 10 suppliers and 20 customers. The distance and vehicle speed data are generated using simulation with random numbers.FindingsSignificant amount of carbon footprint can be reduced through the adoption of eco-efficient vehicle routing with a marginal increase in total transportation cost. Sensitivity analysis indicates that compared to carbon cap, carbon price has more influence on the total cost.Research limitations/implicationsThe model considers mid-sized problem instances. To analyze large size problems, heuristics and meta-heuristics may be used.Practical implicationsThis study provides an analysis of carbon cap and price model that would assist practitioners and policymakers in formulating their policy in the context of carbon emissions.Originality/valueThis study provides two significant contributions to low carbon supply chain management. First, it provides a vehicle routing model under carbon cap and trade policy. Second, it provides a sensitivity analysis of carbon cap and price in the model.

In the context of climate mitigation, biomass has traditionally been viewed as a means to deliver low-carbon energy products. Adding carbon capture and sequestration (CCS) to a bioenergy production process can yield net-removals of CO2 from the atmosphere, albeit at an increased cost. Recently, the Aines Principle was established, stating that at some carbon price, the revenue generated from CO2 removal will exceed the revenue generated from energy production from a given bioconversion process. This principle has only been illustrated for the theoretical conversion of a non-specific biomass source, and has not yet been demonstrated to show real carbon prices that can tip the scale for biomass carbon removal to be more economically favorable than bioenergy production. In this study, we demonstrate the Aines Principle at work in two specific examples of biomass conversion. The first case involves a Chinese municipal solid waste incineration plant, with and without CCS. The second case compares using forestry residue solely for energy production (via gasification), solely for carbon removal (via burial) or both. By comparing the energy and carbon revenue streams under a range of carbon prices, we show that carbon removal revenue can exceed energy revenue at currently available carbon prices below $200/tCO2.

Since carbon price volatility is critical to the risk management of the CO2 emissions trading market, research has focused on energy prices and macroeconomic drivers which cause changes in carbon prices and make the carbon market more volatile than other markets. However, they have ignored whether the impact of carbon price determinants changes when the carbon price is at different levels. To fill this gap, this paper applies a semiparametric quantile regression model to explore the effects of energy prices and macroeconomic drivers on carbon prices at different quantiles. The model combines the advantages of parameter estimation, nonparametric estimation and quantile regression to describe the nonlinear relationship between carbon price and its fundamentals, which do not need to make any assumptions about the random error. Carbon prices are high–tailed and exhibit higher kurtosis, the traditional models which tend to assume that data are normally distributed can’t perform well. Furthermore, the semiparametric model doesn’t need to assume that the data are normally distributed. Therefore, the semiparametric model can effectively model the data. Some new evidence from China’s emission trading scheme (ETS) pilots shows that energy prices and macroeconomic drivers have different effects on carbon prices at high or low quantiles. First, the negative impact of coal prices on carbon prices was greater at the lower quantile of carbon prices in the Shenzhen ETS pilot. However, the effects of coal prices were positive in the Beijing ETS pilot, which may be attributed to great demand for coal. Second, oil prices had greater negative effects on carbon prices at higher quantiles in Beijing and Hubei ETS pilots. This can be attributed to the fact that businesses use less oil when carbon prices are high. For the Shenzhen ETS pilot, the effects of oil prices were positive. Third, natural gas prices have a stronger effect on carbon prices as quantiles increased in the Beijing and Hubei ETS pilots. Lastly, the effects of macroeconomic drivers on carbon prices at low quantiles were stronger in the Shenzhen ETS pilots and higher at the medium quantiles in Beijing and Hubei ETS pilots. These findings suggest that the impact of determinants on the carbon prices at different levels is not constant. Ignoring this issue will lead to a missed warning about the risks of the carbon market. This study will be of positive significance for China’s emission trading scheme (ETS) pilots, in order to accurately monitor the effects of carbon prices determinants and effectively avoid carbon market risks.

Carbon emissions abatement: Emissions trading vs consumer awareness

The asymmetric interdependence of carbon futures and crude oil futures prices in different market conditions remains unsettled in the literature. This study aims to quantify the crude oil price impacts on carbon price across the carbon-oil distribution in Phase III. For this purpose, we employ two novel methods, namely the quantile Granger causality test and the quantile-on-quantile regression methods. To detect the short-, medium-, and long-term impacts of the crude oil price on carbon price, we further decompose the sample series into six components using the wavelet method. Accordingly, we are able to estimate the nexus between carbon futures and crude oil futures prices in different time and frequency domains. Through a series of robustness checks, we find our results stable and robust, indicating that the crude oil impact on carbon price is asymmetric, conditional on the whole carbon and crude oil price distributions. Furthermore, the crude oil impact varies across different time scales, and shows negative signs throughout the whole carbon price distribution in the short term and basically positive signs in the medium and long term. Based on the above findings, we highlight several important policy implications to promote better market regulation and portfolio optimization.

China is pursuing aggressive action to reduce greenhouse gas emissions from its coal-dominated electric power system. Two key strategies are power market reform and carbon pricing. This paper investigate the synergistic effects of these two strategies in reducing CO2 emissions from power system operations. We develop an economic dispatch model to simulate hourly power supply system operation in China Southern Power Grid in 2018 under fifteen carbon pricing scenarios; these reflect a wide range of policy ambition, from today’s carbon prices to much higher ones that aim to instigate aggressive emission mitigation. Our results show that moderate carbon pricing alone is insufficient to effectively reduce CO2 emissions without concurrent power sector reform. With power sector reform and as carbon prices increase, large coal units supplemented by energy storage witness higher use rates as they supplant smaller coal-fired generators, until a carbon price of 300 RMB/TCO2 phases out coal use in favor of natural gas. Only at carbon prices higher than 300 RMB/TCO2 do emissions begin to decrease appreciably. Geographic disparities emerge among the five provinces that comprise the Southern Power Grid, with Guangdong witnessing the most CO2 emission reduction at high carbon prices, while emissions reduction in other provinces are negligible. Carbon pricing also dramatically increases total power system costs, even at low carbon prices. Our results show the necessity of introducing power sector reform and carbon pricing policies concurrently if the goal is to reduce CO2 emissions, with the ultimate goal being a carbon price significantly greater than 300 RMB/TCO2. Because both options will be necessary, our research maps a path to deep emission reductions in China Southern Power Grid for investors, analysts, and policy makers as discussions regarding both reforms accelerate.