Carbon Emission Price Research Articles

Rewarding Countries for Pricing Carbon Emissions: Optimal Mechanisms under Exogenous Budgets

Consider a global institution with an exogenous budget that can reward each developing country based on its tax rate on the combustion of a given fossil fuel. I develop a model in which countries differ in the co-benefits that they derive from emissions reductions and also in their aversion to taxing carbon. Assuming a uniform type distribution and linear demand functions for the fossil fuel, I provide an explicit solution for the optimal mechanism. It can be implemented through a reward payment function of the following form: each country is rewarded based on how much (if at all) its carbon tax exceeds a certain reference level. The reward payment is quadratic in this amount if the global institution's budget is small. For large budgets, there is an additional term that is linear in the amount that the carbon tax exceeds the reference level. Empirical calibrations suggest that carbon pricing reward funds could play a valuable role if the world mobilizes substantial additional funding for supporting emissions reductions in developing countries.

The Effect of Empirical Log Yield Observations on Carbon Storage Economics

An empirical model for log yield from trees is established and applied in microeconomics of carbon storage in a boreal spruce estate. The transition from pulpwood to sawlogs is a smoother function of stem diameter in the empirical data, in comparison to literature values. Correspondingly, the value transition of trees along with increasing size is gentler. Due to price premiums of sawlogs from clearcuttings, all economically feasible treatment schedules terminate in clearcutting. Best capital return rates are gained with two heavy thinnings from above before clearcutting. Present carbon emission prices allow moderate carbon storage increment if the increment is compensated by proportional carbon rent. Doubling the present carbon prices would allow strong carbon storage increments if compensated by carbon rent. Application of nonproportional carbon rent is proposed.

The impact of the global stock and energy market on EU ETS: A structural equation modelling approach

Abstract The industrial revolution has brought about great development in the economy, but it has also increased the dependence on fossil energy. The emissions of CO2 and other greenhouse gases have contradicted economic development and the ecological environment. The establishment of the EU Emission Trading System (EU ETS) has improved the global carbon emission price mechanism, but as a new commodity, its price trend will affect buyers’ risk evaluation. Therefore, it is influential to master the driving factors behind carbon emission prices and make effective predictions. First, the paper points out that the driving factors are divided into macroeconomic risk factors and energy factors. Second, the Bayesian Network is used to select variables and make prediction of carbon prices. The results show that its accuracy exceeds other machine learning algorithms. Third, a structural equation model is used to study the impact of the selected markets on the carbon market. Finally, from the perspective of global carbon emission reduction, the relationship between driving factors and the carbon futures market is explained. The empirical results show that Cotation Assistee en Continu 40, natural gas and Brent crude oil will directly affect the yield of European Union Allowances and Certified Emission Reduction futures, and the Standard Poor 500 and Global Clean Energy Index will indirectly affect the yield of European Union Allowances and Certified Emission Reduction futures. The energy market will affect the carbon market through the intermediary effect of the stock market, in which the clean energy index is the most relevant factor. From the perspective of how to improve the carbon trading system, this paper proposes suggestions for the sustainable development of the world to promote the virtuous cycle of the global carbon emission market and the high-quality development of the global economy.

Stranded investment associated with rapid energy system changes under the mid-century strategy in Japan

Japan’s mid-century strategy to reduce greenhouse gas (GHG) emissions by 80% by 2050 requires rapid energy system changes, which may lead to stranded assets in fossil fuel-related infrastructure. Existing studies have shown that massive stranding of assets in the energy supply side is possible; few studies have involved economy-wide stranded asset analysis. In this study, we estimated stranded investments in both the energy supply and demand sectors in Japan in the context of near-term goals for 2030 and the mid-century strategy. To this end, multiple emission scenarios for Japan were assessed based on various emission reduction targets for 2030 and 2050. The results show that stranded investments in the energy supply sectors occur mainly in coal power plants without carbon capture and storage (CCS), especially in scenarios without enhanced near-term mitigation targets. Increases of stranded investment in demand sectors were observed primarily under stringent mitigation scenarios, which exceed the 80% reduction target. In particular, investment for oil and gas heating systems in the buildings sector may be stranded at levels up to $20 billion US between 2021 and 2050. We further simulated a scenario incorporating a subsidy for devices that do not use fossil fuels as a sector-specific policy; this reduced the amount of stranded investment in the buildings sector. We confirmed the benefit of enhancing near-term mitigation targets to avoid generating stranded investments. These findings support the importance of inclusive energy and climate policy design involving not only pricing of carbon emissions but also complementary cross-sector economy-wide policies.

Renewable energy consumption and robust globalization(s) in OECD countries: Do oil, carbon emissions and economic activity matter?

This study examines the impact of globalization(s) on renewable energy consumption in OECD countries by endogenizing per capita GDP, oil prices and per capita carbon emissions. We use robust globalization(s) as measured by the “classic”, “reconstructed” and “revisited” globalization indexes. The novel method of Machado and Silva Panel quantile regression (2019) approach is used to obtain robust findings for the renewable energy consumption-globalization nexus. The results confirm the presence of a long-run association between renewable energy consumption with globalization(s), per capita GDP, oil prices and per capita carbon emissions. The empirical results also describe that there are positive effects for per capita income, the real price of oil and carbon emissions per capita on the renewable energy consumption. In addition, a higher level of (overall, economics, social and political) classic globalization promotes renewable energy consumption, while the “reconstructed” and “revisited” economic globalization reduces the use of renewable energy consumption, and this finding is also robust to different measures of economic globalization. Moreover, the panel quantile regression reveals that renewable energy consumption increases the domestic economy in the middle (0.50) quantile group of the population through importing more advanced technology and positive spilling over markets, while the lower quantile group and the higher quantile group of the population are using non-renewable (coal, wood) energy because of the livelihood practice that is based on coal and wood (for the lower quantiles group of the population) and for the sake of speedy growth (for the higher quantiles group of the population) that worsens the environmental quality without caring for the contents of globalization.

Carbon emissions market adjustment in the electricity supply sector: A government perspective

The atmospheric warming associated with anthropogenic climate change can be mitigated by pricing carbon emissions and especially through carbon emissions trading schemes (ETS). To this end, a multi-objective bi-level programming model that considers the mutual interactions between the government and power projects is developed to determine an appropriate carbon price to achieve carbon intensity minimization and revenue maximization. Goal programming is employed for the multi-objective programming and an interactive particle swarm optimization and simplex method (IPSO-SM) mechanism is developed to solve the proposed model, the effectiveness of which is verified in a real-life case study. The results suggest that the government can adjust carbon emissions market by auctioning or repurchasing carbon allowances to achieve a balance between environmental protection and economic development, and that the carbon trading decisions of the power projects depended on the comparison of the carbon shadow prices and market carbon price, which constituted the bidding mechanism, formed the carbon price supply and demand curve, and ultimately determined the carbon price.

Optimal Operation Strategies under a Carbon Cap-and-Trade Mechanism: A Capital-Constrained Supply Chain Incorporating Risk Aversion

Many small and medium enterprises (SMEs) with capital constraints often have no access or find it costly to obtain a loan from a bank; the retailer tends to borrow money from other enterprises in the supply chain by trade credit financing. We consider an emission-dependent supply chain with one emission-dependent manufacturer and one capital-constrained retailer in need of financing to explore the optimal operational and environmental strategies of a low-carbon supply chain under trade credit financing. We use a Stackelberg game model to depict the low-carbon supply chain. We analyse the optimal carbon-emission reduction effort, wholesale price, and order quantity in the equilibrium state. The impacts of key parameters, such as the retailer’s internal working capital, the manufacturer’s risk-aversion degree, and the carbon-trading price on the supply chain operation, are analysed. The results show that the retailer’s capital constraint causes the carbon-emission reduction effort, wholesale price, and order quantity to improve synchronously. The supply chain achieves a win-win outcome for both the manufacturer and the retailer when the capital-constrained retailer is funded via trade credit from the manufacturer. The in-depth development of financing is beneficial to the manufacturer but is a disadvantage for the retailer. When the initial carbon-emission quota is low, the manufacturer benefits from a relatively lower carbon-trading price. Otherwise, a higher carbon-trading price is better for the manufacturer. The “carbon-trading price trap” ensures that the retailer’s profit is minimal. We further investigate the scenario in which the manufacturer is risk averse and find that the retailer will purchase fewer products and that the manufacturer will gain less profit to decrease the carbon-emission reduction effort. The manufacturer’s risk aversion is unfavourable to both the economic and environmental outcomes of the whole supply chain. This research provides strategic support for a low-carbon supply chain to carry out operational decisions in the context of enterprise capital constraint. To examine the theoretical results, the data used in the existing literature are further used to simulate the corresponding conclusions. Our research enriches the existing supply chain finance literature and provides decision support for the supply chain core enterprise.

Equilibrium Analysis of a Tax on Carbon Emissions with Pass-through Restrictions and Side-payment Rules

Chile was the first country in Latin America to impose a tax on carbon-emitting electricity generators. However, the current regulation does not allow firms to include emission charges as costs for the dispatch and pricing of electricity in real time. The regulation also includes side-payment rules to reduce the economic losses of some carbon-emitting generating units. In this paper we develop an equilibrium model with endogenous investments in generation capacity to quantify the long-run economic inefficiencies of an emissions policy with such features in a competitive setting. We benchmark this policy against a standard tax on carbon emissions and a cap-and-trade program. Our results indicate that a carbon tax with such features can, at best, yield some reductions in carbon emissions at a much higher cost than standard emission policies. These findings highlight the critical importance of promoting short-run efficiency by pricing carbon emissions in the spot market in order to incentivize efficient investments in generating capacity in the long run.

Optimal Decisions for Two Risk-Averse Competitive Manufacturers under the Cap-and-Trade Policy and Uncertain Demand

With the increasingly serious problem of environmental pollution, reducing carbon emissions has become an urgent task for all countries. The cap-and-trade (C&T) policy has gained international recognition and has been adopted by several countries. In this paper, considering the uncertainty of market demand, we discuss the carbon emission reduction and price policies of two risk-averse competitive manufacturers under the C&T policy. The two manufacturers have two competitive behaviors: simultaneous decision making and sequential decision making. Two models were constructed for these behaviors. The optimal decisions, carbon emission reduction rate, and price were obtained from these two models. Furthermore, in this paper the effects of some key parameters on the optimal decision are discussed, and some managerial insights are obtained. The results show that the lower the manufacturers’ risk aversion level is, the higher their carbon emission reduction rate and utilities. As the carbon quota increases, the manufacturers’ optimal carbon reduction rate and utilities increase. Considering consumers’ environmental awareness, it is more beneficial for the government to reduce the carbon quota and motivate manufacturers’ internal enthusiasm for emission reduction. The government can, through macro control of the market, make carbon trading prices increase appropriately and encourage manufacturers to reduce carbon emissions.

Economic optimization and potential analysis of fuel cell vehicle-to-grid (FCV2G) system with large-scale buildings

The popularization of fuel cell electric vehicles (FCV) is an important measure for solving the global carbon emission reduction problem. However, the overall cost of FCV and the production cost of fuel hydrogen are relatively high, so FCV promotion is slow. Considering that FCV has the characteristics of near-zero carbon emission and high endurance, which is suitable for the vehicle-to-grid (V2G) system, this paper hoped to provide a boost for the promotion of FCV by analyzing the economic potential of the FCV2G system. Firstly, a large-scale commercial building in Japan was selected as the research target and the agent of vehicles to provide a V2G service for the power grid. Then the Monte Carlo simulation method was used to simulate the visiting time and condition of the vehicles. Secondly, the discharge model was established. Combined with the carbon emission price and self-elasticity coefficient of the discharge price, the overall economic optimization model was established. Then, the genetic algorithm was used to optimize the model. With continuous importing of FCVs, the overall economic benefit was improved. Finally, a sensitivity analysis was carried out on the six parameters of daily electricity price, battery cost, fuel cell cost, carbon emission price, power grid carbon emission and hydrogen cost. It was concluded that FCV2G has good economic benefits and high development potential, and the economic benefits of FCV2G will continue to increase with the passage of time.

Modeling Latent Carbon Emission Prices for Japan: Theory and Practice

Climate change and global warming are significantly affected by carbon emissions that arise from the burning of fossil fuels, specifically coal, oil, and gas. Accurate prices are essential for the purposes of measuring, capturing, storing, and trading in carbon emissions at regional, national, and international levels, especially as carbon emissions can be taxed appropriately when the price is known and widely accepted. This paper uses a novel Capital (K), Labor (L), Energy (E) and Materials (M) (or KLEM) production function approach to calculate the latent carbon emission prices, where carbon emission is the output and capital (K), labor (L), energy (E) (or electricity), and materials (M) are the inputs for the production process. The variables K, L, and M are essentially fixed on a daily or monthly basis, whereas E can be changed more frequently, such as daily or monthly, so that changes in carbon emissions depend on changes in E. If prices are assumed to depend on the average cost pricing, the prices of carbon emissions and energy may be approximated by an energy production model with a constant factor of proportionality, so that carbon emission prices are a function of energy prices. Using this novel modeling approach, this paper estimates the carbon emission prices for Japan using seasonally adjusted and unadjusted monthly data on the volumes of carbon emissions and energy, as well as energy prices, from December 2008 to April 2018. The econometric models show that, as sources of electricity, the logarithms of coal and oil, though not Liquefied Natural Gas (LNG,) are statistically significant in explaining the logarithm of carbon emissions, with oil being more significant than coal. The models generally displayed a high power in predicting the latent prices of carbon emissions. The usefulness of the empirical findings suggest that the methodology can also be applied for other countries where carbon emission prices are latent.

Carbon price forecasting with complex network and extreme learning machine

Carbon emission price mechanism is the core issue in carbon emission trading. The carbon emission price fluctuation trend is related to the play of the effectiveness of carbon emission trading market, and directly affects the green and low-carbon behavior of enterprises and residents. Therefore, the prediction of carbon price is of great practical significance. This study presents a new carbon price prediction model by using time series complex network analysis technology and extreme learning machine algorithm (ELM). In our model, we first map the carbon price data into a carbon price network (CPN), and then extract the effective information of carbon price fluctuations by using the network topology, and use the extracted effective information to reconstruct the carbon price sample data. With the reconstructed data and the extreme learning machine algorithm, the carbon price network extreme learning machine model (CPN-ELM) is built. To test the validity of the model, we selected the carbon emission price data of the second, third and transition stages of the European Union Emissions Trading System (EU ETS) for empirical analysis, the results show that CPN-ELM can improve the predictive accuracy of ELM in both level accuracy and directional accuracy. Meanwhile, CPN-ELM prediction model has better robustness when facing the random samples, sample data with different frequencies or sample data with structural changes.

Profitable Emissions-Reducing Energy Storage

While energy arbitrage from energy storage can lower power system operating costs, it can also increase greenhouse gas emissions. If power system operations are conducted with the constraint that energy storage operation must not increase emissions, how does this constraint affect energy storage investment decisions? Two bi-level energy storage investment problems are considered, representing `philanthropic' (profitability-constrained) and profitmaximizing storage investors (PhSI, PMSI). A MILP heuristic is developed to obtain good candidate solutions to these inherently MINLP bi-level problems. A case study is conducted on a 30% renewable system, with sensitivity analyses on the price of storage and the price of carbon emissions. Regardless of the emissions-neutrality constraint, a PhSI installs significantly more energy storage than a PMSI, increasing system flexibility. The effect of the emissions-neutrality constraint in the absence of a carbon price is to reduce the quantity of storage purchased and reduce annual system emissions (~3%), with only minor increases in overall cost (~0.1%). In cases with a carbon price, storage does not tend to increase emissions and the emissions constraint does not tend to decrease storage investment. The emissions-neutrality constraint is seen to deliver similar emissions reductions even in a system with much higher renewable penetration (46%).

Low-carbon Marine Logistics Network Design under Double Uncertainty of Market Demand and Carbon Trading Price

Wang, W.; Ren, Y.; Bian, W., and Jia, X., 2019. Low-carbon marine logistics network design under double uncertainty of market demand and carbon trading price. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 30–39. Coconut Creek (Florida), ISSN 0749-0208.This paper discusses the low-carbon marine logistics network design problem under the uncertainty of market demand and carbon emission price, and aims at the minimum cost of shipping logistics network operation (including carbon emission cost), and constructs a three-level marine logistics network site-distribution model based on scenario. The problem is transformed into an unconstrained optimization problem by the penalty function method, and the artificial bee colony algorithm (ABC) is used to solve the model. The study found that the moderate fluctuation of demand is conducive to the integration effect of the marine logistics network, which reduces the total cost of the logistics network. Finally, the validity of the model is verified by an example analysis of a domestic industrial product manufacturer.

Ratings of The Investment Projects of Arbitrary Durations: New Methodology

In this paper we develop for the first time a new approach to ratings of the investment projects of arbitrary durations, which could be applied to investments of any area of economy and in particular to energy projects.The ratings of such energy projects, as "Turkish stream", "Nord stream-2", energy projects relating to clean, renewable and sustainable energy, as well as relating to pricing carbon emissions (McAleer et al., 2018a,b,c; 2019) could be done using developed here new rating methodologies. In our previous papers the new approach to the ratings of the long–term investment projects has been developed (Filatova et al., 2018). The important features of that consideration are as following: 1) The incorporation of rating parameters (financial "ratios"), used in project rating and playing a major role in it, into modern long–term investment models, 2) The adequate use of discounting of financial flows virtually not used in existing project rating methodologies. Here, for the first time, we incorporate the rating parameters (financial "ratios"), used in project rating, into modern investment models, describing the investment projects of arbitrary durations. This was much more difficult task then in case of the long–term investment projects, considered by us in previous papers. We work within investment models, created by authors. One of them describes the effectiveness of investment project from perspective of equity capital owners, while other model describes the effectiveness of investment project from perspective of equity capital and debt capital owners. New approach allows use the powerful instruments of modern theory of capital cost and capital structure (BFO theory) (Brusov et al., 2015, 2018) and modern investment models, created by the authors and well tested in the real economy to evaluate investment project performance, including energy projects.In our calculations we use Excel technique in two aspects: 1) we calculate WACC at different values of equity costs k0, different values of debt costs kd and different values of leverage level L=D/S, using the famous BFO formula; 2) we calculate the dependences of NPV on coverage ratios as well as leverage ratios at different values of equity costs k0, different values of debt costs kd and different values of leverage level L.

The effects of oil price shocks on the prices of EU emission trading system and European stock returns

This paper examines whether oil price shocks of different origin affect the price of carbon emission allowance traded under the European Union's Emissions Trading System; leading to changes in aggregate and sector-specific European equity returns. The results show that an unexpected oil-supply disruption has an imminent but weak positive effect on carbon emission price, while a positive aggregate demand shock has a strong positive effect on carbon emission price. By contrast, a positive oil-specific (precautionary) demand shock has a negative but weak effect on carbon emission price. These findings are economically important as positive shocks on the emission allowance price trigger a decrease on the aggregate stock return of the European equity market, albeit they trigger a large and persistent increase on European equity returns of oil-related industries with the exception of the Energy sector.

A Two-Stage Green Supply Chain Network with a Carbon Emission Price by a Multi-objective Interior Search Algorithm

This paper presented a new two-stage green supply chain network, in which includes two innovations. Firstly, it presents a new multi-objective model for a two-stage green supply chain problem that considers the amount of shortage in the network, reworking, and carbon-trading cost produced in the green supply chain. Secondly, because of the complexity of this model, it uses a new multi-objective interior search algorithm (MOISA) to solve the presented model. The obtained results of the proposed algorithm were compared with the results of other multi-objective meta-heuristics, namely MOPSO, SPEA2, and NSGA-II. The outcomes demonstrate that the proposed MOISA gives better Pareto solutions and indicates the superiority of the proposed algorithm in most cases. This paper presented a new two-stage green supply chain network, in which includes two innovations. Firstly, it presents a new multi-objective model for a two-stage green supply chain problem that considers the amount of shortage in the network, reworking, and carbon-trading cost produced in the green supply chain. Secondly, because of the complexity of this model, it uses a new multi-objective interior search algorithm (MOISA) to solve the presented model. The obtained results of the proposed algorithm were compared with the results of other multi-objective meta-heuristics, namely MOPSO, SPEA2, and NSGA-II. The outcomes demonstrate that the proposed MOISA gives better Pareto solutions and indicates the superiority of the proposed algorithm in most cases.

Economics of stationary electricity storage with various charge and discharge durations

Abstract Electricity storage encompasses a disparate list of technologies such as pumped-storage hydroelectricity, compressed-air energy storage, chemical batteries and flywheels. These technologies can provide the electricity system with heterogeneous services of energy transfers across months, weeks, days or intra-days, power transfers for an hour, a few minutes or seconds, and can assist operators in load following, frequency control, and uninterrupted power supply. The paper presents a unified economic analysis of these technologies and services. We underline the role of charge and discharge durations as a criterion for economic segmentation of technologies and services. We highlight the complementary value of storage in electricity systems with a high share of low variable cost and low carbon generation (nuclear, hydro, wind power, solar photovoltaic). We also underline the limited substitution value of storage for generation with high variable cost (gas combustion-turbines or gas-oil motor engines), given the high cost of state-of-the-art storage technologies, the current low cost of fossil fuels and low price of carbon emissions.

Influencing factors and fluctuation characteristics of China’s carbon emission trading price

The environmental deterioration and resulting climate change have become one of the major challenges that human has faced in recent years. Carbon emission trading, as an effective economic tool to deal with climate change issues, has attracted widespread attention. As a major carbon emitter, China plays an important role in combating global climate change. Based on the carbon emission trading price data of China’s Hubei Emission Exchange, a Vector Auto-Regressive (VAR)-Vector Error Correction (VEC) model is first used to investigate the dynamic relationship between energy price, macroeconomic indicators, air quality, and carbon emission trading price. The results show that there is a long-term equilibrium relationship between carbon emission trading price and these indicators. When the carbon emission price is too high and deviates from the long-term equilibrium value, it will slowly decline to reach the long-term equilibrium value. The price of carbon emission trading is largely affected by macroeconomic indicators among all these influencing factors. In addition, a Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model is used to explore the fluctuation characteristics of China’s carbon emission trading price. It is found that the return series of carbon emission price are consistent with the characteristics of financial time series, such as fluctuation aggregates, spikes and thick tails, and non-normal distribution. There is a positive leverage effect for the fluctuation of China’s carbon emission price. It is further found that external bad news has a greater impact on the fluctuation of China’s carbon emission trading price than good news.

Modeling and Testing Volatility Spillovers in Oil and Financial Markets for the USA, the UK, and China

The main purpose of the paper is to analyze the conditional correlations, conditional covariances, and co-volatility spillovers between international crude oil and associated financial markets. The prices of oil and its interactions with financial markets make it possible to determine the associated prices of financial derivatives, such as carbon emission prices. The approach taken in the paper is different from others in the literature; the purpose is to examine the usefulness of modeling and testing volatility spillovers in the oil and financial markets. The paper investigates co-volatility spillovers (namely, the delayed effect of a returns shock in one physical or financial asset on the subsequent volatility or co-volatility in another physical or financial asset) between the oil and financial markets. The oil industry has four major regions, namely North Sea, the USA, Middle East, and South-East Asia. Associated with these regions are two major financial centers, namely the UK and the USA. For these reasons, the data to be used are the returns on alternative crude oil markets, returns on crude oil derivatives, specifically futures, and stock index returns in the UK and the USA. Given the importance of the Chinese financial and economic systems, the paper also analyzes Chinese financial markets, where the data are more recent. The USA and China are the world’s two largest economies and the UK is the world’s sixth largest economy (and second in the existing EU) behind the USA, China, Japan, Germany, and India. Moreover, the USA and the UK are associated with WTI and Brent oil, respectively. One of the purposes of the paper is to examine how China might be different from the USA and the UK, which seems to be borne out in the empirical analysis. Based on the conditional covariances to test the co-volatility spillovers, dynamic hedging strategies will be suggested to analyze market fluctuations in crude oil prices and associated financial markets.