Search Results (24)

Based on the economic and climate policy uncertainty index and the price data of major carbon emission trading markets from May 2014 to August 2023, this paper uses the generalized autoregressive conditional heteroskedasticity and mixing data sampling (GARCH-MIDAS) model to analyze the impact of policy uncertainty on carbon market price volatility. The results indicate the following: (1) The price volatility in the Hubei carbon market is influenced by both economic and climate policy uncertainties, while the Guangdong market is only affected by climate policy uncertainty, and the Shenzhen carbon market is only affected by economic policy uncertainty. (2) Before the establishment of the national carbon market, the carbon market prices in Hubei were impacted by both policy uncertainties, while Guangdong and Shenzhen carbon markets were only affected by climate policy uncertainties. (3) On the contrary, after the establishment of the national carbon market, only the Shenzhen carbon market was affected by both policy uncertainties, and the price volatility in the Guangdong and Hubei carbon markets was not affected by policy uncertainties. The above research conclusions are helpful for regulatory agencies and policymakers to assess the future direction of the pilot carbon market and provide an empirical basis for preventing and resolving policy risks. At the same time, the proposed GARCH-MIDAS model effectively solves the inconsistent frequency problem of policy uncertainty and carbon price volatility, providing a new perspective for the study of factors affecting carbon market volatility. Full article

This study evaluates the economic viability of the South Pasopati Coal Project in Indonesia, addressing market volatility, carbon pricing policies, and the country’s energy transition towards Net Zero Emissions (NZE). Given Indonesia’s reliance on coal and the increasing global shift toward renewable energy, traditional valuation methods such as Discounted Cash Flow (DCF) may not adequately capture uncertainty and strategic flexibility. The study applies Real Options Valuation (ROV), integrating Monte Carlo Simulation (MCS) and Binomial Lattice Modeling, to assess project feasibility under various scenarios. The research compares three valuation scenarios: the base scenario (eastern route), an alternative scenario (western route), and a carbon pricing scenario. Results indicate that while the DCF method estimates a positive Net Present Value (NPV) for the base scenario, it fails to incorporate price volatility risks. The ROV method, however, captures managerial flexibility and provides a more robust valuation, showing an Expanded NPV (ENPV) that better reflects market uncertainties. Findings suggest that implementing ROV improves decision-making, particularly in volatile markets. The study underscores the necessity for Indonesia to adopt more flexible valuation frameworks to enhance investment decisions in the coal sector while aligning with international environmental standards. Full article

Energy storage technology is a critical component in supporting the construction of new power systems and promoting the low-carbon transformation of the energy system. Currently, new energy storage in China is in a pivotal transition phase from research and demonstration to the initial stage of commercialization. However, it still faces numerous challenges, including incomplete business models, inadequate institutional policies, and unclear cost and revenue recovery mechanisms, particularly on the generation and grid sides. Therefore, this paper focuses on grid-side new energy storage technologies, selecting typical operational scenarios to analyze and compare their business models. Based on the lifecycle assessment method and techno-economic theories, the costs and benefits of various new energy storage technologies are compared and analyzed. This study aims to provide rational suggestions and incentive policies to enhance the technological maturity and economic feasibility of grid-side energy storage, improve cost recovery mechanisms, and promote the sustainable development of power grids. The results indicate that grid-side energy storage business models are becoming increasingly diversified, with typical models including shared leasing, spot market arbitrage, capacity price compensation, unilateral dispatch, and bilateral trading. From the perspectives of economic efficiency and technological maturity, lithium-ion batteries exhibit significant advantages in enhancing renewable energy consumption due to their low initial investment, high returns, and fast response. Compressed air and vanadium redox flow batteries excel in long-duration storage and cycle life. While molten salt and hydrogen storage face higher financial risks, they show prominent potential in cross-seasonal storage and low-carbon transformation. The sensitivity analysis indicates that the peak–valley electricity price differential and the unit investment cost of installed capacity are the key variables influencing the economic viability of grid-side energy storage. The charge–discharge efficiency and storage lifespan affect long-term returns, while technological advancements and market optimization are expected to further enhance the economic performance of energy storage systems, promoting their commercial application in electricity markets. Full article

Efficient supply–demand management in Product-as-a-Service (PaaS) markets requires tools to evaluate pricing strategies while integrating sustainability goals like reuse, efficiency, and carbon footprint reduction. This paper introduces a declarative modeling framework aimed at balancing the three pillars of profitability, cost optimization, and sustainability in PaaS markets. The framework addresses risks such as equipment failure, usage variability, and economic fluctuations, helping providers optimize pricing and operating costs while enabling customers to manage expenses. A declarative model is developed to assess the PaaS market balance to determine optimal leasing offers and requests for quotations. A case study is used to validate the framework, involving devices with specific rental prices and failure rates, as well as customer expectations and budget constraints. Computational experiments demonstrate the model’s practical applicability in real-world scenarios and it can be used by PaaS providers to develop competitive leasing strategies, policymakers to assess market stability, and enterprises to optimize procurement decisions. The findings show that the framework can guide decision making, offering insights into the impact of new technologies, compatibility conditions for leasing offers, and strategies for balancing providers’ profits and customers’ costs. The proposed framework has broad applicability across industries such as manufacturing, healthcare, logistics, and IT infrastructure leasing, where efficient resource allocation and lifecycle management are crucial. Full article

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. Full article

Climate risk assessment is critical for organisations, especially in sectors such as the processed food sector in Colombia. This study addresses the identification and assessment of the main climate transition risks using an approach that combines fuzzy logic with several multi-criteria decision-making methods. This approach makes it possible to handle the inherent imprecision of these risks and to use linguistic expressions to better describe them. The results indicate that the most critical risks are price volatility and availability of raw materials, the shift towards less carbon-intensive production models, increased carbon taxes, technological advances, and associated development or implementation costs. These risks are the most significant for the organisation studied and underline the need for investments to meet regulatory requirements, which are the main financial drivers for organisations. This analysis highlights the importance of a robust framework to anticipate and mitigate the impacts of the climate transition. Full article

As the proportion of new energy sources, such as wind power, in the electricity system rapidly increases, their participation in spot market competition has become an inevitable trend. However, the uncertainty of clearing price and wind power output will lead to bidding deviation and bring revenue risks. In response to this, a bidding strategy is proposed for wind farms to participate in the spot market jointly with carbon capture power plants (CCPP) that have flexible regulation capabilities. First, a two-stage decision model is constructed in the day-ahead market and real-time balancing market. Under the joint bidding mode, CCPP can help alleviate wind power output deviations, thereby reducing real-time imbalanced power settlement. On this basis, a tiered carbon trading mechanism is introduced to optimize day-ahead bidding, aiming at maximizing revenue in both the electricity spot market and carbon trading market. Secondly, conditional value at risk (CVaR) is introduced to quantitatively assess the risks posed by uncertainties in the two-stage decision model, and the risk aversion coefficient is used to represent the decision-maker’s risk preference, providing corresponding strategies. The model is transformed into a mixed-integer linear programming model using piecewise linearization and McCormick enveloping. Finally, the effectiveness of the proposed model and methods is verified through numerical examples. Full article

In an environment marked by dual carbon goals and substantial fluctuations in coal market prices, coal power generation enterprises face an urgent imperative to make scientifically informed decisions regarding production management amidst significant market uncertainties. To tackle this challenge, this paper proposes a methodology for optimizing electricity generation side market decisions and assessing risks using quantile data-driven information-gap decision theory (QDD-IGDT). Initially, a dual-layer decision optimization model for electricity production is formulated, taking into account coal procurement and blending processes. This model optimizes the selection of spot coal and long-term contract coal prices and simplifies the dual-layer structure into an equivalent single-layer model using the McCormick envelope and Karush–Kuhn–Tucker (KKT) conditions. Subsequently, a quantile dataset is generated utilizing a short-term coal price interval prediction model based on the quantile regression neural network (QRNN). Interval constraints on expected costs are introduced to develop an uncertainty decision risk measurement model grounded in QDD-IGDT, quantifying decision risks arising from coal market uncertainties to bolster decision robustness. Lastly, case simulations are executed by using real production data from a power generation enterprise, and the dual-layer decision optimization model is solved by employing the McCormick–KKT–Gurobi approach. Additionally, decision risks associated with coal market uncertainties are assessed through a one-dimensional search under interval constraints on expected cost volatility. The findings demonstrate the effectiveness of the proposed research methodology in cost optimization within the context of coal market uncertainties, underscoring its validity and economic efficiency. Full article

Pricing decisions for construction and demolition waste recycling are severely hampered by consumer uncertainty in assessing the value of recycled building materials. This paper uses a construction and demolition waste (CDW) recycling utilization model that consists of a building materials manufacturer and a building materials remanufacturer and compares both the prices and the profits under different carbon tax scenarios, i.e., consumer risk-averse and risk-neutral scenarios. The main conclusions are as follows. (1) The optimal price of traditional products is always negatively correlated with consumer risk aversion. Unlike traditional products, the optimal price of recycled building materials is negatively related to the degree of consumer risk aversion in the case of a low carbon tax; the opposite conclusion is obtained in the case of a high carbon tax. (2) When the abatement cost coefficient is below the threshold and the carbon tax is low, the profits of the building materials manufacturer and remanufacturer show a U-shaped trend with consumer risk aversion; in the case of a high carbon tax, the profits of the two enterprises are positively correlated with consumer risk aversion. In addition, when the abatement cost coefficient is above the threshold, there is an interval in which the profits of the building materials manufacturer are positively correlated with consumer risk aversion in the case in which the carbon tax satisfies this interval. In all the other cases, there is a U-shaped trend in profits and consumer risk aversion levels for both the building materials manufacturer and the remanufacturer. Full article

Business parks account for 30% of China’s total carbon emissions. Exploring emissions reduction approaches for business parks is crucial to achieve a net-zero emissions target, as well as for achieving a representative example for all types of emissions entities. Business parks mainly adopt two types of emissions reduction approaches: energy-saving renovations and purchasing carbon reduction products. However, there are limited studies focusing on the optimal combinations of the two approaches for reaching net-zero emissions and evaluating the cost effectiveness. To find a feasible and quantified way to build net-zero business park, a comprehensive path decision model is proposed. The problem is broken down into two parts: the optimal carbon reduction portfolio and the optimal electricity saving were researched. For the optimal product portfolio, the Markowitz theory is employed to balance the risk of carbon reduction products with the expected cost. In the part of optimal electricity saving, considering a ten-year life cycle, the total cost includes renovation investment, carbon reduction products cost, and cost saving of electricity consumption reduction. Based on the energy consumption, technical, and price data, the combination of energy-saving renovations and carbon reduction products is optimized. The model suggests a business park can save 24% of energy consumption through renovation investment and purchase CCER as 66% of the carbon reduction product portfolio. Taking only purchasing carbon reduction products as a benchmark to assess economic efficiency, implementing an optimized level of energy-saving renovation is found to save 16% of the comprehensive cost for the life cycle required to achieve zero carbon emissions. This model provides a new comprehensive optimization idea that will help future parks make decisions to achieve zero-carbon emission targets. Full article

Driven by the “double carbon” goal, the sale of financial assets at reduced prices by firms due to carbon emission constraints is bound to aggravate the uncertainty and volatility of carbon trading among firms, and potentially create counterparty risk contagion. In view of this, this paper considers the sensitivity of the transaction of corporate financial assets, the transaction price of carbon quotas, and corporate carbon performance; constructs a network model for the risk contagion of carbon quota counterparties; theoretically discusses the risk formation and infection mechanism of carbon quota counterparties; and calculates and simulates the evolutionary characteristics of the risk contagion of carbon quota counterparties. The main research conclusions are as follows. (1) In the interfirm debt network, the sensitivity to the price of selling the financial asset, the probability of credit risk contagion of carbon quotas among firms, the cumulative proportion of assets sold, and the proportion of rational investors in the financial market exert a decreasing phenomenon on the risk of carbon quota counterparties. In addition, the corporate carbon performance shows a marginal increasing phenomenon. (2) When multiple factors intersect, the proportion of rational investors in the financial market has the greatest influence on the formation of the carbon quota counterparty risk, whereas the effect of corporate carbon performance has the least. Corporate carbon risk awareness has the greatest effect on the risk contagion of carbon quota counterparties, whereas the trading price of the carbon quota has the least influence. In addition, the total score of the interfirm assessment has a great impact on the trend and range of the risk contagion of carbon quota counterparties. (3) Corporate carbon risk awareness and the carbon quota trading price have a marginally decreasing effect on the risk contagion of carbon quota counterparties, and corporate carbon performance and the total score of interfirm assessment have a marginally increasing effect. This study has important theoretical and practical significance for preventing interfirm counterparty risk contagion under the double carbon target. Full article

Given the spatial heterogeneity of the social-economic situations across different regions in China, the decomposition of emission reduction targets should be designed according to the actual characteristics of the industrial economy. There is concern about the loss of industrial competitiveness and leakage of CO₂ emissions if just seven pilot carbon markets operate independently, so the national carbon market of the power sector was established in 2021. In this study, a China two-region CGE model including Guangdong (GD) and the rest of China (ROC) is built on an analysis of the long-term effects of CO₂ prices in industrial sectors at the target 2030 peak. Based on this model, we constructed one business-as-usual scenario and six comparison carbon tax scenarios to quantify the CO₂ cost impact for a wide range of manufacturing sectors and identify specific economic activities that face relatively high CO₂ costs between the two regions. Based on the China two-region CGE model, the risks of leakage and competitiveness distortions in these potentially exposed sectors are qualitatively assessed. The results show that chemical, nonferrous metal, and machinery are GD’s competitive sectors, and agriculture, food, textile, paper, cement, construction, and service belong to ROC’s competitive industry. Both GD and ROC need to further unify carbon pricing policies at the same time to effectively coordinate the carbon intensity reduction target and industrial development, which is 2.6% and 3.2% of the severe GDP loss compared with BaU when implementing carbon tax policy. The results can support the setting of the carbon tax and industrial competitiveness promotion policy and with a strong reference to support the provincial emission reduction path. Full article

Energy price fluctuations pose a significant risk and uncertainty to financial investments for new developments in conventional power and freshwater cogeneration facilities. This study attempts to address the problem of making robust valuation for low-carbon energy project investments subject to multi-dimensional price risk, particularly looking at some key research questions: (a) how does the correlation structure, or independence, between the price risks affect the project value; and (b) does adding flexibility in investment enhance or worsen the project valuation, given (a). This study identified three price factors with significant fluctuations that impact conventional power generation, namely: wholesale electricity spot price, natural gas spot price, and CO₂ market price. The price factors were used to construct a multidimensional risk model and evaluate investment decisions for cogeneration project expansion in the future based on a low-carbon energy mix. To this end, five cogeneration configurations using combined-cycle gas turbine (CCGT) integrated with solar photovoltaics (PV) and carbon capture and storage (CCS) technologies were assessed. A combined price risk was initially estimated by transforming the given price factors representing maximum covariance using principal component analysis (PCA). The trend and volatilities in the major principal component scores (the combined price risk indicator) were modelled using the geometric Brownian motion stochastic process, whose parameters were determined and then used to perform time-series simulation and generate multiple realisations of the principal component. A back transformation was then applied to obtain the simulated values representing future uncertainties in the price factors. The effect of price risk and uncertainties were subsequently evaluated using a recombining binomial lattice model for real options analysis (ROA). There were financial gains when PV was mixed with conventional natural gas-fired technology. Investment in cogeneration configurations with (a) 25% PV share provided a 53% gain in the extended net present value (e–NPV); and (b) 50% PV share provided a 124% e–NPV gain when compared to the baseline cogeneration system with no PV shares. The analyses demonstrate that PV technology is a better hedging option than CCS against future market uncertainty and price volatility. Full article

Forest carbon offset programs have suffered from low landowner uptake, in large part to their long duration. A recent innovation in forest carbon offsets is the use of short period delays to harvest, which extend the rotation age of the stand beyond what is optimal for timber alone and increase sequestered carbon. Here, we assess the economic value of a short period delay “option pricing” in forest harvest with price uncertainty using a binomial option approach, accounting both for timber and carbon. Results from an option pricing model showed that landowners can generate considerably higher revenue with managerial flexibility along with the additional revenue from carbon offset programs. These results can help forest landowners make proper ownership decisions to withstand the risk and uncertainty associated with stumpage prices, while benefiting from carbon offset revenues. Full article

Human civilization depends on energy sources, mainly fossil fuels. An increase in the prices of fossil fuels and their exhaustibility limit economic growth. Carbon dioxide emission causes global environmental problems. Global crises (including COVID-19) have sharpened food and energy supply problems. The decentralized energy supply systems as well as the expedition of the application of renewable energy may solve these challenges. The economic shift to renewable power generation intensifies the competition between food crop production and green energy for land. This paper applied an open-source spatial-based model to quantify the solar power generation (the ground-mounted photovoltaic panels) for the southern regions of Poland (the Opole region) and Ukraine (the Mykolaiv region). The model used technical, economic, and legal constraints. This study compared economic indicators of the solar power generation and the crop production projects for rain-fed land. The net present value (NPV) and the profitability index (PI) were used for the economic evaluation. Additionally, the coefficients of variation were determined to assess investment risks. The use of r.green.solar model to find the spatial distribution of the reduction of carbon dioxide emission was the novelty of this study. The analysis revealed that the PV projects have higher NPV, but lower PI compared to the crop production. The PV projects have lower coefficients of variation. This fact testifies that these projects are less risky. Full article