Emission Price Research Articles

Nonnegligible transition risks towards net-zero economy: Lessons from green finance initiatives in China.

Owing to critical policy significance, a growing body of literature has been predominantly concentrating on the social welfare benefits brought by green finance (GF) initiatives. However, there is a paucity of research that quantifies the economic costs of GF initiatives on carbon reduction, raising the increasing concerns about the irreconcilable climate-economy trade-offs. To end this, the present study systematically investigates the influence of GF initiatives on the carbon-related marginal abatement cost (MAC) using two competing hypotheses: regulatory versus technical effects. We first utilize an improved non-radial slacks-based measure analysis approach to determine the shadow price of CO2 emissions, and then identify the real impact of GF initiatives on the MAC of CO2 emissions using the progressive implementation of GF pilot programs as a quasi-natural design. To avoid non-parallel trends in pre-treatment periods and biased inference caused by the small number of units exposed, we utilize the newly developed synthetic DID approach to establish the causality. We find robust evidence that GF initiatives significantly increase the MAC of CO2 emissions by about 310 yuan per ton in affected pilots compared to the counterparts. We further uncover that GF initiatives have tenuous effects on decreasing the carbon abatement costs via the green innovation channel, the technical effect is largely overpowered by the regulatory effect, which substantially pushes up the carbon abatement costs, ultimately leading to a total rise in the carbon abatement costs. Overall, our study offers new insights into the previously under-explored cost side of GF initiatives on the real economy. In particular, we argue that GF regulation is one of the nonnegligible channels by which transition risks get embedded in net-zero economy.

Integrating crop and dairy production systems: Exploring different strategies to achieve environmental targets.

The integration between crop and livestock production systems (ICLS) at regional level is seen as a pathway for more sustainable food production. The objective was to assess the effects on farm structure, economic performance and environmental impact of an ICLS with varying constraints on agricultural emissions, changes in land use and a lower external input use as means to achieve environmental targets. A linear optimization model was used for economic optimization of ICLS under different scenarios for the case of crop and dairy production systems on sandy soils in the Netherlands. The model was combined with a farm nutrient balance and life-cycle assessment to determine the impact on nutrient surpluses and greenhouse gas (GHG) emissions. Environmental costs for society were calculated based on available prices for GHG emissions and nutrient surpluses. As a reference, specialized crop and livestock systems without integration were optimized. An economically driven ICLS under current Dutch policy without additional constraints was modelled. This strategy was economically interesting and increased gross margins, but the environmental costs for society were higher when compared to the reference systems without integration. Thereafter, four scenarios were modelled representing varying constraints as a means to achieve environmental targets. These scenarios were hypothesized to lower the environmental costs for society by targeting a reduction in agricultural emissions, lower external input use or changes in land use. Stricter constraints on greenhouse gas (i.e., a reduction of at least 30%) and ammonia (i.e., a reduction of at least 37%) emissions and de-intensification strategies (e.g., lower milk production per cow, reduced concentrate intake per cow and lower mineral N fertilizer use in the dairy sub-system) reduced gross margins of the ICLS up to €34,395yr-1. Furthermore, it resulted in a substantial reduction in the gross margin for the dairy sub-system, while gross margins for the crop sub-system substantially increased. However, these scenarios reduced environmental impacts and associated costs for society substantially up to €60,045yr-1. For a scenario with land use constraints (i.e., stricter constraints on area proportions for potatoes and sugar beets and an increase in the area used for permanent grassland) there were still economic benefits while there was also reduction in the environmental costs of production. The results help to further design and assess the potential contribution of ICLS to improve the sustainability of the current farming system.

Techno-economic pathway for green hydrogen adoption in thermal applications across US small and medium manufacturing sectors

This study presents an optimization framework for designing a renewable hydrogen production system to meet the annual thermal energy demand of US small and medium-sized manufacturers (SMMs). The proposed system integrates solar photovoltaics, battery storage, and an electrolyser and the use of a mixed-integer linear programming model to determine the optimal sizing of components to minimize system cost while satisfying hydrogen demand under varying natural gas replacement scenarios. The optimization results indicate that increased PV and battery capacities are required for higher shares of hydrogen, while the size of the electrolyser remains constant within each industry. The economic analysis reveals significant cost escalation at higher hydrogen shares, with photovoltaics contributing more than half of the net present cost. Although a carbon pricing mechanism could help make the proposed system economically feasible, a cost analysis indicates that very high emission prices would be required to render hydrogen economically competitive with natural gas. The study thus recommends the use of targeted incentives and renewable grid integration to meet the challenges of transitioning SMMs to hydrogen and accelerate decarbonization.

From farms to tables: Quantifying the effect of emissions pricing on canadian food prices

AbstractWe examine the effect of emissions pricing on the cost of Canadian food. We describe emissions pricing policies relevant to the agriculture and food sectors and the differing design details of various provincial systems and the federal Greenhouse Gas Pollution Pricing Act. To quantify the potential effect of such policies, we use a detailed input‐output model of Canada's economy to estimate both the direct and indirect cost increases across sectors. We also explore how exemptions and policy design can mitigate what would otherwise be larger effects. In particular, imported inputs, generous exemptions for most direct emissions in primary agriculture, and special provisions for large industrial emitters all dampen the effect. Overall, we find that emissions pricing at $80 per tonne could potentially increase the cost of domestically produced food by approximately 0.8% on average. Combined with imported food that is not directly affected by emissions pricing, we find an average effect of approximately 0.5%. While we abstract from general equilibrium responses, our analysis suggests emissions pricing in Canada has only a modest effect on food costs.

Analyzing the carbon pricing-leakage nexus through structural gravity estimation

Carbon emissions drive climate change, and failure to reduce these emissions could lead to disastrous consequences. Countries impose carbon pricing as an instrument to reduce carbon emissions. However, carbon leakage is a counteractive mechanism where countries with higher carbon emission prices may see their sectors outsource production to countries with lower prices or lose competitiveness and market share to foreign companies with lower production costs. This study confirms that fuel excise taxes are the leading channel of carbon leakage. Structural gravity estimates show that carbon pricing based on carbon taxes and emission trading schemes (ETS) does not cause carbon leakage, but fuel excise taxes and ETS exemptions have a significant impact. This finding is supported when looking at carbon prices in different sectors. Thus, policymakers should consider adjusting fuel excise taxes accordingly.

Time-Varying Spillover Effects of Carbon Prices on China’s Financial Risks

As China’s financial markets become increasingly integrated and the carbon market undergoes financialization, the impact of carbon emission price fluctuations on financial markets has emerged as a key area of systemic risk research. This study employs the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model and the optimal Copula function to investigate the dynamic correlation between carbon prices and China’s financial markets. Building on this, the Monte Carlo simulation and Copula CoVaR models are used to explore the spillover effects of carbon price volatility on China’s financial markets. The findings reveal the following: (1) Carbon price fluctuations generate spillover effects on all financial markets, but the intensity varies across different markets. The foreign exchange market experiences the strongest spillover effect, followed by the bond market, while the stock and money markets are relatively less affected. (2) The optimal Copula functions differ between the carbon market and China’s financial markets, indicating heterogeneous characteristics across regional markets. (3) There is a degree of interdependence between the carbon market and various sub-markets in China’s financial system. The carbon market has the strongest positive correlation with the commodity market and a relatively high negative correlation with the real estate market. These findings underscore the importance of integrating carbon price volatility into financial risk management frameworks. For policymakers, it highlights the need to consider market stability measures when crafting carbon emission regulations. Market managers can leverage these insights to develop strategies that mitigate risk spillover effects, while investors can use this analysis to inform their portfolio diversification and risk assessment processes.

Asymmetric Impact of Carbon Emission, Oil Price Shocks on Economic Growth in Nigeria (1981 – 2022)

The study examines the asymmetric impact of carbon emissions and oil prices on economic growth in Nigeria. The research is conducted using annual time series data running from 1986 to 2019. The study employs Nonlinear autoregressive distributed lag models (NARDL) as the estimation technique. The variables used in the analysis were Gross Domestic Product (GDP), Carbon Emission (CO2), and Oil Price (OP). The NARDL result shows that carbon emission has a negative and significant impact on economic growth in the long run and the oil price has a positive and significant impact on economic growth in both the short-run and long-run. Based on the empirical outcomes of the result obtained, the following recommendations were offered: The implementation of an economic diversification policy from oil reliance towards dependence on other sustainable energy types (renewable energy resources) should be sustained and strengthened especially. The private sector should also tap into the opportunities that are available in the oil sector, such as the decarbonization programme in order to reduce carbon emissions from fossil fuels.

Two-stage distributionally robust optimization operation of virtual power plant considering the virtual energy storage of electric vehicles

Virtual Power Plant (VPP) is a key to aggregate various distributed energy sources. With the vigorous rise of various distributed energy sources, the direct access of large-scale electric vehicle load will increase the complexity of VPP coordinated operation. Hence, this paper proposes a VPP optimization method for Electric Vehicle Virtual Energy Storage (EV-VES). Firstly, the travel characteristics of electric vehicles are analyzed, and EV-VES model is established to coordinate and manage the charge-discharge behavior of EV. Secondly, the “carbon charge rate” model of energy storage (ES) is introduced to establish the relationship between carbon emission and electricity price, and the VPP operation model considering the “carbon charge rate” of energy storage is established. Finally, the two-stage robust optimization operation model of VPP is constructed, Wasserstein distance is used to describe the confidence set of uncertain probability distribution of wind power generation and load, and the uncertainty of system source and load are described by the confidence set. The Column and Constraint Generation (C&CG) algorithm was used to determine the optimal operational benefit solution. The effectiveness of the proposed VPP optimal operation model was validated through case study.

Predictive Powers of Carbon Emissions and Oil Price on Stock and Foreign Exchange (FX) Markets Using Multivariate Recurrent Neural Networks (RNN) Model

The predictive power of environmental factors such as CO2 levels and Brent oil price was evaluated on the stock market (S&P 500) and foreign exchange (FX) rates for market currency pairs (EUR/USD) and emerging market currency pairs (MXN/USD) with recurrent neural networks (RNN). The S&P 500 is a collection of well-established companies in the United States representing the macroeconomic health of the nation and the global economy. The FX market is a global decentralized over-the-counter (OTC) market used to determine the spot price of currency pairs. A highly leveraged market usually trades within a specific price range. Since stocks and FX pairs are highly correlated to macroeconomic factors, it was hypothesized that environmental factors such as CO2 levels and oil prices also have predictive power due to their close causal relationship with anthropological economic activities. To verify the predictive power, an RNN model was built, and a bi-directional neural network with an internal state was used to process data sequences. The performance of RNN was quantified by measuring the residual prediction from the true value. Although the study at its current state might need further statistical rigor, it concluded that environmental factors increased the predictive power for the S&P 500 while decreasing it for the DM FX pair and the EM FX pair showed mixed results.

Production of Hydrogen from Biomass with Negative CO2 Emissions Using a Commercial-Scale Fluidized Bed Gasifier

Biomass gasification, as a thermochemical method, has attracted interest due to the growing popularity of biofuel production using syngas or pure hydrogen. Additionally, this hydrogen production method, when integrated with CO2 capture, may have negative CO2 emissions, which makes this process competitive with electrolysis and coal gasification. This article presents the results of process and economic analyses of a hydrogen production system integrated with a commercial, fluidized-bed solid fuel gasification reactor (SES technology—Synthesis Energy Systems). With the use of a single gasification unit with a capacity of 60 t/h of raw biomass, the system produces between 72.5 and 78.4 t/d of hydrogen depending on the configuration considered. Additionally, assuming the CO2 emission neutrality of biomass processing, the application of CO2 capture leads to negative CO2 emissions. This allows for obtaining additional revenue from the sale of CO2 emission allowances, which can significantly reduce the costs of hydrogen production. In this analysis, the breakthrough price for CO2 emissions, above which the hydrogen production costs are negative, is USD 240/t CO2.

Differential Game Analysis of Carbon Emission Reduction Effect and Price Strategy Considering Government Intervention and Manufacturer Competition

ABSTRACTGovernments and enterprises are paying more and more attention to carbon emissions. Considering the dynamic of carbon emissions reduction (CER) and the government intervention, this study discusses the optimal CER effort level, price, and government intervention intensity in a two‐echelon supply chain consisting of a government and two manufacturers. The two manufacturers have two competitive behaviors: Cournot and Stackelberg. Two differential game models are constructed for the two different behaviors, and the optimal decisions under the two models are obtained. The comparisons of these optimal solutions are analyzed, and the influence of some parameters on the optimal solution in the two models is investigated under two scenarios. Furthermore, the optimal government intervention intensity is obtained with the goal of maximizing government utility. The results show that the Stackelberg game allows manufacturers to achieve higher profits and CER but is disadvantageous to consumers, and the manufacturer as the leader has a first‐mover advantage. Fierce market competition leads to greater CER and profits, but higher prices reduce consumer surplus. Larger penalties can promote enterprises to reduce carbon emissions when carbon emissions are large. Compared with the Cournot behavior game, the Stackelberg allows manufacturers to obtain higher profits and CER, but the prices are higher that are detrimental for consumers. The fierce market competition is good for manufacturers, the environment, and the society, but it reduces the consumer surplus. The low CER efficiency causes high costs and reduces the manufacturer's motivation to CER, which harm the environment and reduce profits. The government intervention is negatively correlated with the intensity of market competition and the sensitivity of manufacturers to policies.

Asymmetric relationship between carbon market and energy markets

This paper examines the asymmetric returns spillovers and time-frequency causality between the carbon emissions market and the energy market. To this end, we apply the time-varying asymmetry spillovers and Granger causality over the spectrum approaches. This research uses daily price indices of natural gas, gasoline, gas oil, heating oil, crude oil, coal, petroleum, kerosene, propane, and diesel to denote the energy market and the European Union Emissions Trading System (i.e., certificate prices for CO2 emissions) to represent the carbon market. Using historical time-series data from May 18, 2011, to September 23, 2020, the study reveals interesting and convincing empirical results showing that the carbon and energy markets are dynamically and asymmetrically connected. Further results show that the carbon market predominantly explains positive or negative returns in the energy market. Regarding volatility transmission, the study demonstrates that the carbon market is a primary net receiver of good or bad volatility transmitted from the energy market, such as crude oil, petroleum, heating oil, diesel, and kerosene.

Techno-economic optimization and feasibility of PCM-based seasonal thermal energy storage systems for district heating and cooling

Phase change materials (PCM) are an attractive seasonal thermal energy storage solution for load shifting due to relatively high energy density. Nevertheless, the choice of the right design parameters, such as storage size and phase-change temperature, is nontrivial, as these are tightly linked to the expected seasonal operation of the storage. This paper presents a combined design and operation optimization framework for sizing PCM-based seasonal thermal storage, which can capture dynamic behaviour of the storage in sensible and latent phases, and its impact on the efficiency of connected heat pumps and chillers. The proposed method, formulated as a mixed integer quadratically-constrained programming problem, was applied to a case study of heating-dominated district. It was found that the optimal PCM melting temperature equals to the heating demand supply temperature, thus removing the need of a heat pump to discharge the storage. The optimal operation of storage requires a full charging and discharging cycle of both latent and liquid phase. Higher CO2 emission price does not lead to a significant reduction of CO2 emissions, but the storage size does, leading to a higher heating coverage ratio of the storage, and consequently CO2 emissions and cost savings, which can be further enhanced with PV integration. The economic analysis indicates that, unless other benefits such as storage volume reduction are accounted for, PCM cost should drop by a factor 4 to make this solution economically viable for seasonal storage.

Techno economic study of floating solar photovoltaic project in Indonesia using RETscreen

The utilization of solar energy is crucial for the advancement of sustainable power generation on a worldwide scale, driven by environmental concerns and the depletion of fossil fuels. Indonesia's goal is to achieve carbon neutrality by 2060 and it is aggressively advocating for solar energy, which includes the implementation of new methods such as floating photovoltaic (PV) systems. This study evaluates the Techno-Economic Feasibility of Indonesia's Cirata 145 MW floating solar PV project by employing RETScreen technology. The objective is to improve the long-term financial stability, decrease greenhouse gas emissions, and suggest viable choices for improvement. Examining three scenarios that involve alterations in carbon emissions, energy pricing, and loan interest rates demonstrates different levels of project feasibility. The introduction of carbon tax emission pricing has a substantial impact on the feasibility of projects. This study provides useful insights into doing techno-economic feasibility assessments using RETScreen for floating photovoltaic (PV) systems. It demonstrates how modifying parameters can effectively mitigate project risks.

Effect of electricity policy uncertainty and carbon emission prices on electricity demand in China based on mixed-frequency data models

This paper first constructs the electricity policy uncertainty (EPU) in China with textual methods and analyses the effect of the EPU and carbon emission prices (CEPs) on the total electricity demand. This paper also forecasts the demand for electricity in China with three mixed-frequency data models. The results show that the EPU index efficiently captures the uncertainty of China's electricity policy. The effects of EPU and CEPs on electricity demand are significant, and incorporating them into the forecasting model will improve the accuracy and timeliness. Moreover, compared with the ARMA model and LSTM neural networks, mixed-frequency data models perform better in electricity demand forecasting.

U.S. vertically integrated electric utility greenhouse gas emissions and carbon risk premiums around the Paris Accord

We study the pricing of greenhouse gas emissions of vertically integrated producers of electricity around the Paris Accord (PA). We study whether emissions are priced by financial markets, providing a market-based incentive for firms to reduce their carbon footprints and if the heightened attention on climate change post-Paris Accord (PA) impacts the size of the “carbon risk premium.” We focus on electricity generators, because they are responsible for the largest share of emissions and emissions reductions in the U.S. and are highly exposed to regulatory, physical, and stranded asset risks. We find the cost of carbon risk is reflected in the returns of vertically integrated electric utilities. The post-PA period provides the strongest evidence that carbon risk is priced. We find that equity markets provide incentives for power producers to reduce emissions, as reductions in emissions are associated with reductions in required returns on equity (increases in equity market values). The challenge for regulators is how to respond in rate cases. Lowering a utility's regulated return to reflect lower market estimates of the return on equity would dilute the market-based incentive for emissions reductions. Adding a longer-term return incentive for continued investment in emissions reductions would reinforce the market incentive.

A near real-time carbon accounting framework for the decarbonization of maritime transport

The recently proposed marine greenhouse gas (GHG) emission pricing mechanism has pressured shipping companies and regulators to adopt effective methods for the real-time monitoring of carbon emissions. This study proposes a Near Real-Time (NRT) carbon accounting framework that leverages machine learning models to enable carbon emission tracking at a 15-minute time interval. The framework incorporates critical factors, such as ship navigation characteristics, weather, and sea conditions to achieve accurate carbon accounting. We validate the framework’s efficacy through a case study of four mega-container ships of varying sizes and navigation scenarios. Our results show a maximum cumulative error of 5.83% for all ship navigation scenarios, even without critical data, and during the most extended voyages of their respective services. The proposed framework provides a new perspective on the decarbonization application of ship energy efficiency prediction research. By integrating it with a cloud-computing platform, shipping companies can enhance their voyage planning and route adjustment to optimize operational efficiency and reduce carbon footprints. Using this framework, international maritime transport regulators can develop an early warning system for carbon emissions to coordinate and improve environmental sustainability practices in the shipping industry.

Life cycle GHG emissions and economic viability of two levulinic acid production processes from biomass: A case study of Japan and Canada

We evaluated CO2 equivalent (CO2eq) greenhouse gas (GHG) emissions and minimum selling price of levulinic acid (LA) produced in two biomass-waste-based processes: the AlCl3/choline chloride (ChCl) process, and the formic acid (FA) process, with catalysts recycling. Six scenarios were synthesized to compare the performances of the two processes in Japan and Canada. In the AlCl3/ChCl process, the total GHG emission was 11.35–11.56 kg-CO2eq/kg-LA and those from the energy input to the pretreatment and ChCl production were 5.22 and 3.90 kg-CO2eq/kg-LA, respectively. In the FA process, the total GHG emission was 9.46–9.68 and 22.29–22.51 kg-CO2eq/kg-LA for 60 wt% and 80 wt% FA, respectively. The operational emissions for makeup FA input were 7.65 and 20.80 kg-CO2eq/kg-LA (60 wt% and 80 wt%, respectively), which accounted for more than 80% in all scenarios. The optimization of the product purge volume, FA concentration in the pretreatment, and FA production using biomass and/or renewable energy are critical parameters to reduce overall environmental impacts of the processes. The liquid content of the solid residue (moisture, water soluble organic matters, and catalyst) had insignificant influences on the GHG emission and minimum selling price. In the FA process, combustion of solid residue can compensate the GHG emissions from the reaction and separation units.

How to implement the 2023 IMO GHG strategy? Insights on the importance of combining policy instruments and on the role of uncertainty

In its 2023 strategy for reducing GHG emissions from ships, the International Maritime Organization aims for the uptake of zero or near-zero GHG emissions technologies, fuels or energy to represent at least 5 %, striving for 10 %, of the energy used by 2030. Measures proposed to reach this target combine two kinds of policy instruments: an emission intensity standard and GHG emissions pricing. Proposals vary in terms of the design of the pricing scheme and the subsequent uncertainty that would affect the price signal. This paper assesses the extent to which emissions pricing effectively complements a more restrictive standard on the intensity of GHG emissions to boost transition to low-carbon vessels. Attention is paid to the role of the volatility of the price signal of GHG emissions and fuels prices. For this purpose, the paper applies a multiple real options model to the decision to renew a ship under various sources of uncertainty. Simulations carried out based on a case study show that the price signal effectively complements the more restrictive standard but that the decarbonization objectives are only achieved with a quite ambitious level of pricing. The role of the volatility of the price signal of GHG emissions appears negligible compared to other sources of volatility.

CLIMATE POLICY UNCERTAINTY AND CARBON PRICE: EVIDENCE FROM FREQUENCY AND QUANTILE PERSPECTIVE

Changes in climate policy are posing enormous challenges to carbon markets. However, the intricate link and time-frequency features between climate policy uncertainty (CPU) and carbon emission price (CEP) have not been extensively explored in the current literature. This paper aims to reveal the dynamic relationship between the two under different time frequencies and quantiles by using a wavelet-based quantile-on-quantile regression (QQR) method. Nine sets of variable pairs with various temporal frequencies are constructed, such as short-term CPU and long-term CEP. The results demonstrate that their coefficients would change from positive to negative or irrelevant, or conversely, under different quantiles and frequencies. China has been a global focus and an interesting case since its relentless efforts towards addressing climate change and constructing a carbon market, which makes one of the contributions of this paper. Additionally, time frequencies combined with different quantiles are employed to explore the connection between CPU and CEP, revealing nonlinear and asymmetric features between variables, and enriching the theoretical influencing mechanisms. Therefore, the government should avoid drastic policy changes, and make full use of policy guidance effect. Besides, top-design and market-oriented reforms for the carbon market are needed.