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Carbon pricing policies can play an important role in reducing global greenhouse gas emissions. However, policymakers often cite the lack of public support as a major obstacle to adopting and expanding the coverage of such policies. Using data from a large cross-country survey of about 28,500 people across 28 countries conducted during the summer of 2022, we show that providing information about the costs and benefits of carbon pricing can swing public support. Highlighting the efficacy and revenue recycling potential of carbon pricing increases support, especially in emerging market economies. In contrast, highlighting costs reduces support, but less so for people who report being already significantly impacted by climate change. Moreover, people adopt more negative views of subsidies for renewable energy and low-carbon technologies when the salience of their costs is increased, with the shift being more pronounced for people, especially in advanced economies, who believe that effective climate polices require global efforts. Our results suggest that there is scope to increase public support for climate mitigation measures by providing people with the policy information they may lack. Key policy insights: Support for climate change mitigation policies can be increased through information campaigns that clearly communicate the effectiveness and co-benefits of these policies. Given that the costs associated with climate policies are a key public concern, policymakers should provide clear explanations on how these costs are offset by long-term benefits and potential revenue recycling mechanisms. Increasing public awareness of the immediate human and economic losses caused by climate change at the local level can strengthen support for climate policies despite economic costs. Encouraging local and community-centric approaches to climate action by can help address concerns regarding collective action problems and coordination failures, and enhance support for national policy measures. [ABSTRACT FROM AUTHOR]

ObjectivesIt is important to improve the processing ability of uncertain parameters in power planning.MethodsA robust power planning model based on cardinality constrained uncertainty set under carbon trading condition was proposed. To reduce the degree of uncertainty, the uncertainty scenario set with cardinality constraints was constructed, and then the concept of “uncertainty budget” was introduced to redefine the best and worst scenarios. Based on duality theory, the robust model with max-min-max three-level programming structure was transformed into equivalent mixed-integer linear programming, which was solved by Baron solver in GAMS.ResultsThe cost of carbon trading can promote a lower proportion of thermal power generation and a higher proportion of renewable energy generation. When the uncertainty budget is larger, the objective function value is lower, and the decision-making style is more conservative.ConclusionsThe power planning strategy based on robust optimization under carbon trading conditions can improve the ability to handle uncertain parameters. The planning results are robust and feasible, providing decision support for decision-makers in situations of information uncertainty.

Due to concerns about carbon leakage and sectoral competitiveness, the European Union (EU) proposed implementing the carbon border adjustment mechanism (CBAM). The effectiveness and potential negative consequences of CBAM have aroused extensive discussion. From the perspective of the economy-wide analysis, this study uses a global computable general equilibrium model to explore the rationality of CBAM from the aspects of socioeconomic impact and the effects of promoting climate mitigation. Furthermore, the potential alternative mechanism of CBAM is proposed. The results show that CBAM can reduce the EU's gross domestic product (GDP) loss; however, the GDP loss in all other regions increases. Moreover, CBAM raises household welfare losses in most regions, including the EU. Second, although CBAM can reduce the marginal abatement cost in eight regions, it comes at the cost of greater economic losses. Furthermore, the economic and household welfare cost of raising emissions reduction targets in regions like the USA and Japan is substantially higher than the impact of passively accepting the CBAM; therefore, CBAM's ability to drive ambitious emission reduction initiatives may be limited. Finally, for the potential alternative mechanism, from the perspective of reducing economic cost and household welfare losses, the EU could implement domestic tax cuts in the short-term and promote global unified carbon pricing in the long-term.

Climate and land‐use change pose unprecedented threats to ecosystems, economies, and communities worldwide. To help mitigate the climate crisis, restoration is a rapidly growing industry used to offset carbon emissions. The most common approach is to plant fast‐growing monocultures with the aim of sequestering as much carbon as possible in the shortest time. However, there has been little economic analysis of planting options that explicitly address short and long‐term ecological risks such as fire, disease, and environmental change. Here we develop a method for quantifying ecological risks from fire to sequestration investments and show how these risks can be factored into an analysis of long‐term financial returns relative to opportunity costs. In the case study presented, we find that the apparent advantage of fast‐growing monoculture plantations is likely to be outweighed by the long‐term fire risks to the carbon stored in them. Our analytical framework provides a widely applicable approach to comparing planting options against each other and other land uses, considering key uncertainties. With climate change already manifesting through extreme weather events, rising sea levels, and shifting wildlife populations, our framework can be used to make informed decisions about the best solutions to increase carbon sequestration, reduce ecological risks, and reduce climate impacts with greater certainty. [ABSTRACT FROM AUTHOR]

Beijing is one of the earliest pilot low-carbon cities in China. It was one of the first cities in China to establish a pilot carbon market to achieve this goal. As an emerging market, China's carbon pricing mechanism is not yet complete. In this context, it is crucial for market managers and companies to predict carbon prices. This study uses a Prophet-EEMD-LSTM model to predict the carbon price in the Beijing carbon market, which significantly improves prediction performance. The advantage of this hybrid model is that it considers the particularities of carbon prices including trends, cyclical changes, and volatility. Considering that the carbon market has multiple complex characteristics, the carbon price is decomposed into multiple simple sequences using the Prophet and EEMD models. These simple sequences were predicted using an LSTM model. The hybrid model outperformed both econometric and single-machine learning models in terms of carbon price prediction. Based on the findings of this study, market managers and companies can take appropriate measures to prevent carbon price risks. These findings are conducive to the smooth operation of the carbon market, thereby providing sustainable support and guidance for the development of low-carbon cities. [ABSTRACT FROM AUTHOR]

Research on climate change mitigation has increasingly considered carbon pricing, with these efforts concentrating on reductions in carbon dioxide (CO2) emissions. Our comprehensive cross-country analysis extends this focus by quantitatively evaluating the effects of carbon pricing on four major pollutants: CO2, nitrous oxide (N2O), methane (CH4), and particulate matter (PM). We use regressions and introduce entropy balancing to this research area. Analyzing data from 132 countries from 1992 to 2019, we find that carbon pricing is associated with an average annual reduction in CO2 emissions by 3 percentage points. A one-unit increase in a coverage-weighted carbon price is associated with reductions in N2O emissions by approximately 0.1 percentage points. A shorter panel for 2010–2017 shows a larger impact of 0.3 percentage points for PM. These findings underline the efficacy of carbon pricing not just in curtailing CO2 but in significantly mitigating other harmful pollutants on a global scale. Reductions in pollutants beyond CO2 provide further motivation for policymakers to pursue carbon pricing. [ABSTRACT FROM AUTHOR]

Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In energy system transformation studies, the assumption of linearly rising CO2 prices is common and based on the decreasing issued European allowances (EUA). The actual auction price for allowances is influenced by market dynamics, resulting in non-linear variations in CO2 prices due to the possibility of banking allowances. This study explores the impact of non-linear CO2 price trajectories on the European electricity market. Various scenarios, such as increasing prices with the market interest rate, early price peaks, collapse, or fluctuations, are examined until 2030. The study quantifies the effects on power plant investments, profitability, consumer costs, and government revenues. The findings reveal that early and high price signals lead to an earlier transition with 7 % more wind power plants but at 4 % higher costs. Conversely, lower initial prices delay power plant investments in wind power plants by 6 % and result in 21 % higher emissions. Compensation for climate impact costs can yield a positive cost-benefit analysis in scenarios with early and high price signals when emissions are avoided through mechanisms like the Market Stability Reserve (MSR).

Abstract Predicting carbon prices is crucial for the growth of China's carbon trading industry. This paper proposes a residual correction model that considers multiple influencing factors. First, the best historical data and main external factors input by the model are determined by using the partial autocorrelation function and Spearman correlation analysis, and the carbon price forecasting index system is constructed. Second, the whale optimization algorithm (WOA) is utilized to determine the optimal parameters of the extreme gradient boosting (XGBoost), and the WOA‐XGBoost model is built to perform preliminary carbon price forecasts and obtain the residual series. Finally, the carbon price residual series undergoes decomposition into multiple components utilizing the complete ensemble empirical mode decomposition for subsequent forecasting and the aggregation of outcomes. Experiments are conducted to predict two carbon trading markets in Hubei and Guangzhou, and a feature importance analysis is performed. The results indicate that the proposed hybrid model consistently outperforms the comparative models in terms of prediction accuracy. Furthermore, it is revealed that historical carbon prices and European Union carbon prices are the key factors influencing the prediction of carbon market prices.

Sustainable forest management is important to maintain the ability of forests to grow and provide a range of products and environmental services while retaining their natural features. We find that income, radiata pine prices, trade, agricultural expansion, and environmental policies have a significant impact on timber harvesting in New Zealand. Increase in income, radiata pine prices, number of farms, horticulture plantation and bank loans lead to an increase in timber harvesting. On the other hand, environmental policies, especially those to mitigate climate change, decrease timber harvesting. [ABSTRACT FROM AUTHOR]

This paper employs a propensity score matching approach to construct a control group and estimate the impact of the CETS pilot policy, a low-carbon financial policy, on corporate green innovation and its impact mechanism in a difference-in-difference manner. The results show that the CETS pilot policy has a significantly positive effect on corporate green innovation. The higher the penalty degree and the carbon price, the more obvious the promotion of the green innovation of pilot enterprises. The mechanism test shows that the improvement of corporate green innovation is mainly due to the incentive effect rather than the anti-driving effect of the CETS pilot policy, that is, the policy promotes corporate green innovation by providing innovation resources and enhancing the willingness to innovate. Further analysis shows that only in regions where local governments have less competitive pressure can the CETS pilot policy effectively promote enterprise innovation resources and that a close and clean government–business relationship can help strengthen the promotion effect of the CETS pilot policy on the willingness of enterprises to innovate. Furthermore, this paper introduces its theoretical framework as a strategic tripod to explore the friction in the process of the CETS pilot policy affecting corporate green innovation from the perspective of the industry environment and corporate resources. This research shows that a lack of industry green technology and corporate human capital may hinder the positive impact of the CETS pilot policy on corporate green innovation. Finally, this study found that the CETS pilot policy has no significant impact on the quality of corporate green innovation, and the lack of industry green technology and corporate human capital may hinder the CETS pilot policy from improving the quality of corporate green innovation. [ABSTRACT FROM AUTHOR]

Sustainably managing municipal solid waste (MSW) is a global challenge since the existing MSW processing procedure releases greenhouse gases and is economically unfriendly. An emissions trading scheme (ETS) is a market-based approach for policymakers to reduce sectoral carbon emissions while maintaining economic efficiency. Hitherto, no waste optimisation framework links ETS to carbon reduction and identifies the trade-offs between the cost of carbon credit trading and waste expenses, rendering doubt of ETS's effectiveness in the waste sector. In this study, an MSW-ETS framework is developed to analyse the potential impact of ETS on the MSW management system incorporating multiple waste types, disposal and treatment facilities, and waste-based products. A multi-objective optimisation model is integrated with the MSW-ETS framework to achieve mutual environmental and economic benefits. Malaysia is selected as an illustrative case study considering its unsustainable MSW disposal and the recent carbon trading plans announced by the government. Simulation of the carbon prices alters Malaysia's MSW management configuration at 363 MYR/tonne CO2-eq, providing insights on developing the carbon price floor for a Malaysian ETS. Six MSW management configurations under different scenarios that can achieve up to 160% GHG emissions reduction were also proposed. The MSW-ETS framework integrated with the multi-objective optimisation model could be replicated by other countries planning to phase the waste sector into ETS coverage. [ABSTRACT FROM AUTHOR]

This research aims to analyze the techno-economic and environmental aspects of retrofitting carbon capture and storage (CCS) technology on the existing 330 MWe pulverized coal (PC) power plant. Modeling simulations on existing PC and retrofitting PC CCS with variations in biomass co-firing (wood pellet) were carried out using the Integrated Environment Control Model (IECM) version 11.5 software. An amine-based post-combustion capture was used in this study. Coal and biomass co-firing at PC CCS reduce the net power output and thermal efficiency. Carbon neutrality occurs at 10% biomass co-firing on PC CCS. There was a 164% increase in the levelized cost of electricity (LCOE), from 0.0487 USD/kWh on PC to 0.1287 USD/kWh on PC CCS. A sensitivity analysis of fuel prices shows that at a fuel price of 25 USD/t, the LCOE of PC CCS is 0.0953 USD/kWh or higher than Indonesia's national weighted LCOE of 0.0705 USD/kWh. The LCOE of PC CCS can be lower than the national weighted LCOE when the carbon price is higher than 80 USD/t CO2. [ABSTRACT FROM AUTHOR]

The conventional approaches to constructing Prediction Intervals (PIs) always follow the principle of 'high coverage and narrow width'. However, the deviation information has been largely neglected, making the PIs unsatisfactory. For high-risk forecasting tasks, the cost of forecast failure may be prohibitive. To address this, this work introduces a multi-objective loss function that includes Prediction Interval Accumulation Deviation (PIAD) within the Lower Upper Bound Estimation (LUBE) framework. The proposed model can achieve the goal of 'high coverage, narrow width, and small bias' in PIs, thus minimizing costs even in cases of prediction failure. A salient feature of the LUBE framework is its ability to discern uncertainty without explicit uncertainty labels, where the data uncertainty and model uncertainty are learned by Deep Neural Networks (DNN) and a model ensemble, respectively. The validity of the proposed method is demonstrated through its application to the prediction of carbon prices in China. Compared with conventional uncertainty quantification methods, the improved interval optimization method can achieve narrower PI widths. [ABSTRACT FROM AUTHOR]

The effectiveness of carbon price in emissions trading schemes (ETS) is an important issue in China in the context of its ambitious climate change goals. This paper adopts a staggered difference-in-differences model to estimate whether China's ETS reduced CO2 emissions while maintaining economic growth even under low carbon prices. The results indicate that despite the low carbon prices, the ETS effectively reduced CO2 emissions without undermining the economy. Specifically, an increase of $1 in the carbon price reduced CO2 emissions by 1.69% and increased the per capita GDP by $286. The carbon price primarily achieved emission reduction and promoted economic development through channels, such as technological innovation, foreign direct investment, energy mix, and industrial structure. Carbon leakage to neighboring regions was not evident. Heterogeneity analysis showed that the environmental effects of the carbon price were more pronounced in regions with higher levels of economic development and CO2 emissions. Conversely, the economic effects of the carbon price were more pronounced in regions with lower levels of economic development and CO2 emissions. The carbon price achieved significant economic effects in regions that solely adopted the free allocation mode of emission allowances, while regions that used a combination of free allocation and auctioning experienced substantial emission reduction effects. Despite low carbon prices, China's ETS effectively reduced CO2 emissions without compromising economic growth. This finding provides new empirical evidence for the effectiveness of carbon price as well as decision support for the future promotion of ETS. Technological innovation, FDI, energy mix, and industrial structure are crucial channels through which the carbon price achieves emission reduction and promotes economic development. The absence of carbon leakage to neighboring regions suggests that ETS is an internally effective mechanism for emission control. Heterogeneity analysis showed that regional characteristics and allowance allocation modes can influence the effectiveness of carbon price, thus emphasizing the importance of targeted policy design and appropriate allocation methods. [ABSTRACT FROM AUTHOR]

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Predicting carbon prices is crucial for the growth of China's carbon trading industry. This paper proposes a residual correction model that considers multiple influencing factors. First, the best historical data and main external factors input by the model are determined by using the partial autocorrelation function and Spearman correlation analysis, and the carbon price forecasting index system is constructed. Second, the whale optimization algorithm (WOA) is utilized to determine the optimal parameters of the extreme gradient boosting (XGBoost), and the WOA‐XGBoost model is built to perform preliminary carbon price forecasts and obtain the residual series. Finally, the carbon price residual series undergoes decomposition into multiple components utilizing the complete ensemble empirical mode decomposition for subsequent forecasting and the aggregation of outcomes. Experiments are conducted to predict two carbon trading markets in Hubei and Guangzhou, and a feature importance analysis is performed. The results indicate that the proposed hybrid model consistently outperforms the comparative models in terms of prediction accuracy. Furthermore, it is revealed that historical carbon prices and European Union carbon prices are the key factors influencing the prediction of carbon market prices. [ABSTRACT FROM AUTHOR]

The growth of the global shipping industry has increased the interest in the environmental impact of this sector. The International Maritime Organization adopted the initial Greenhouse Gas strategy for reducing GHG emissions from ships at the 72nd Marine Environment Protection Committee in April 2018. In this study, we carried out a life cycle assessment of nine production pathways of alternative fuels, including LNG, ammonia, methanol, and biofuels, and conducted an economic analysis considering the life cycle carbon pricing of each fuel pathway. Our results indicate that biomass-based FT-diesel, e-methanol, and e-ammonia are the most environmentally friendly, with GHG reductions of 92%, 88.2%, and 86.6%, respectively. However, our net present value analysis of ship life cycle cost considering carbon price indicated that using those fuels would not be cost-effective during the target period of study. Sensitivity analysis was performed by changing the life cycle carbon pricing from the baseline scenario, and we investigated the approximate years for when these alternative fuels will become more cost-effective compared to conventional fossil fuels. Further, to provide practical implications for shipping stakeholders, we analysed the effect of blending the same kinds of fuels with different production pathways. [ABSTRACT FROM AUTHOR]

Beijing is one of the earliest pilot low-carbon cities in China. It was one of the first cities in China to establish a pilot carbon market to achieve this goal. As an emerging market, China’s carbon pricing mechanism is not yet complete. In this context, it is crucial for market managers and companies to predict carbon prices. This study uses a Prophet-EEMD-LSTM model to predict the carbon price in the Beijing carbon market, which significantly improves prediction performance. The advantage of this hybrid model is that it considers the particularities of carbon prices including trends, cyclical changes, and volatility. Considering that the carbon market has multiple complex characteristics, the carbon price is decomposed into multiple simple sequences using the Prophet and EEMD models. These simple sequences were predicted using an LSTM model. The hybrid model outperformed both econometric and single-machine learning models in terms of carbon price prediction. Based on the findings of this study, market managers and companies can take appropriate measures to prevent carbon price risks. These findings are conducive to the smooth operation of the carbon market, thereby providing sustainable support and guidance for the development of low-carbon cities.

Regime-switching process reflects mechanistic transition states and forecasts extreme carbon price volatility; this article tends to investigate the regional regime-switching behaviors of China's carbon price using the Markov switching model. Our empirical results demonstrate that carbon prices in Beijing, Shanghai, Shenzhen, Guangdong, and Hubei exhibit significant regime-switching for the period from January 2, 2014 to April 30, 2022; our results are similar with the regime-switching behavior of European Union's carbon and energy price. Shenzhen's carbon prices generate greater market fluctuations than those of Beijing, Shanghai, Guangdong, and Hubei. The switching probabilities of carbon prices from low-variance to high-variance state in Shanghai and Beijing are greater than those of Guangdong, Shenzhen, and Hubei. Annual compulsory allowance submissions, allowances allocation corrections, phase-switching between regional and nationwide ETS pilots, and sophisticated energy markets are the main driving factors of regime-switching behavior in China's carbon prices. [ABSTRACT FROM AUTHOR]

This paper estimates the influences of different industries on the carbon prices and selects the industries with high market power in the carbon market. To realize price discovery in the carbon market and improve the market efficiency, we suggest that industries with higher market power should be included in the market prior to other industries. Using the LASSO regression to analyze the data from the pilot markets of China's ETS and related industrial product outputs, we find that among all related industries, the petrochemical and chemical industries affect the carbon price the most and hence should be included in China's ETS with priority. The power generation industry, though regarded as the major entity of emission control, imposes weaker influences on the carbon price. Industries that may be incorporated in China's ETS later include the transportation equipment manufacturing and the mining and metal industries. [ABSTRACT FROM AUTHOR]

Based on data from A-share listed companies in the Shanghai and Shenzhen markets from 2009 to 2020, this study used the PSM-DID (Propensity Score Matching-Difference in Differences) method to empirically examine the impact of carbon emissions trading scheme (ETS) on corporate financial performance and its effect mechanisms. The empirical results indicate that the implementation of ETS effectively enhances corporate financial performance among pilot companies. Additionally, the results indicate that carbon prices have a moderating effect between ETS and corporate financial performance, whereby higher carbon prices strengthen the impact of ETS on corporate financial performance. The influence mechanisms find that ETS improves corporate financial performance primarily driven by promoting corporate green innovation capacity and enhancing corporate social responsibility. Notably, only the sample group with higher carbon prices exhibits this effect. Overall, the implementation of the ETS contributes to improving the ecological environment and enhancing business operations, thus playing a pivotal role in reconciling environmental benefits with economic interests. [ABSTRACT FROM AUTHOR]

The dual carbon target is a two-stage carbon reduction goal proposed by China, while the bridge maintenance strategy does not consider the need for sustainable development. Therefore, this article studies the optimization of bridge maintenance timing under China's dual carbon goals. Firstly, this paper aims to minimize the total cost of maintenance and carbon emissions, considering the continuous effects of carbon pricing and emissions in the context of the dual carbon goals. The CHINAGEM-E model is employed to predict carbon prices, and a preventive maintenance decision-making method for highway bridges is established. Secondly, based on the theory of material residual strength, a degradation model for the technical condition of highway bridges is constructed. Finally, an in-depth case analysis of an in-service highway bridge is conducted to derive optimal maintenance solutions under three scenarios. In comparison to scenarios considering only maintenance costs or those based on benchmark carbon prices, the comprehensive maintenance cost under the dual carbon targets is the highest. In the total maintenance cost, carbon emission costs constitute over 50%, emphasizing the need for increased attention to carbon emission cost studies in future maintenance research. The methodology proposed in this paper is the first to connect carbon prices with the timing of preventive maintenance for bridges, providing a more scientific and sustainable basis for future highway bridge maintenance decisions. [ABSTRACT FROM AUTHOR]

Emission trading scheme (ETS) is popular to abate anthropogenic emissions throughout the world. Previous researchers focused on evaluating ETS policy effect, but ETS design is usually neglected because ETS is already mostly sophisticated worldwide. This is not the case in China, as the Chinese nationwide ETS (CNETS) came into effect in July 2021. Implemented for a brief period, the CNETS lacks implementation details and thus may not achieve mitigation targets cost-effectively. In this paper, we attempt to narrow the research gap by comprehensively designing the CNETS. Our research framework is based on a dynamic recursive computable general equilibrium (CGE) model. The CGE model results show that the appropriate CNETS should include the coverage of the electricity generation and manufacturing sectors, higher carbon price (175 ${CNY} / {t} {CO}_2$), quota allocation based on the carbon intensity in the previous year, higher quota decline factor (2%) and time-decreasing free quota ratio. Although we have only designed the Chinese ETS in this paper, the research framework may become a paradigm of designing appropriate ETS.

Abstract This study uses TIMES model to assess Indonesia’s power sector’s carbon price impact from 2020 to 2050 and the price needed by 2030 to meet the Paris Agreement NDC target. Four scenarios are used to model the impact of carbon price up to 2050: no carbon price, Indonesia’s current price of USD 2.02/tCO2e, ICPF middle- and high-income countries, USD 50/tCO2e and USD 75/tCO2e. Four price scenarios—10, 25, 35, and 150 USD/tCO2e —are added to better understand the carbon price’s effects. As carbon prices rise, installed capacity and power generation will shift to lower-carbon technology. Ultracritical coal, gas-fired, solar, geothermal, and hydropower plants will replace subcritical coal. Investment, fixed, and variable costs would exceed BaU with a higher carbon price. 2.02 to 25 USD/tCO2e can start the coal-to-gas switch but not significantly change the generation profile. The generation will change significantly above 35 USD/tCO2e. Carbon emissions peak lower with rising carbon prices. USD 25 carbon price reduces emissions significantly; a carbon price below that is costly and ineffective. Indonesian Law No. 16 of 2016 ratified the Paris Agreement NDC, committing Indonesia to reduce greenhouse gas emissions by 29% by 2030 or 41% with international assistance. Energy sector emissions need to decrease by 11% for a 29 percent reduction and 14% for a 41 percent reduction. A 29% reduction requires USD 39.65/tCO2e carbon price, while a 41% reduction requires USD 43.78/tCO2e. These prices are still within the reasonable ICPF price limit for Indonesia to approach the middle-income country price floor.

Green innovation has been the crucial and fundamental channel for efficient emission reduction and high-quality realization of the “Dual Carbon” goals. This paper provides novel evidence for the industry-level effect of Emission Trading Scheme (ETS) on green innovation. Specifically, adopting the supply chain Stackelberg model and the multi-period DID model, we demonstrate both theoretically and empirically that the ETS has an overall significant promotion effect on green innovation both in compliance and non-compliance industries. Surprisingly, the promotion effect in non-compliance industries will be stronger, forced by the costs pass-through from up-stream compliance industries. Furthermore, we also find that the carbon price level can amplify the positive effect of the ETS on industry green innovation in the short run, while the price volatility weakens the effect of the ETS in the long run. Our findings shed light on the inherent effects of ETS on green innovation and suggest that policymakers should stabilize the carbon prices so as to encourage green innovation.

We utilize a coupled economy–agroecology–hydrology modeling framework to capture the cascading impacts of climate change mitigation policy on agriculture and the resulting water quality cobenefits. We analyze a policy that assigns a range of United States government’s social cost of carbon estimates ($51, $76, and $152/ton of CO2-equivalents) to fossil fuel–based CO2 emissions. This policy raises energy costs and, importantly for agriculture, boosts the price of nitrogen fertilizer production. At the highest carbon price, US carbon emissions are reduced by about 50%, and nitrogen fertilizer prices rise by about 90%, leading to an approximate 15% reduction in fertilizer applications for corn production across the Mississippi River Basin. Corn and soybean production declines by about 7%, increasing crop prices by 6%, while nitrate leaching declines by about 10%. Simulated nitrate export to the Gulf of Mexico decreases by 8%, ultimately shrinking the average midsummer area of the Gulf of Mexico hypoxic area by 3% and hypoxic volume by 4%. We also consider the additional benefits of restored wetlands to mitigate nitrogen loading to reduce hypoxia in the Gulf of Mexico and find a targeted wetland restoration scenario approximately doubles the effect of a low to moderate social cost of carbon. Wetland restoration alone exhibited spillover effects that increased nitrate leaching in other parts of the basin which were mitigated with the inclusion of the carbon policy. We conclude that a national climate policy aimed at reducing greenhouse gas emissions in the United States would have important water quality cobenefits. [ABSTRACT FROM AUTHOR]

This study uses TIMES model to assess Indonesia's power sector's carbon price impact from 2020 to 2050 and the price needed by 2030 to meet the Paris Agreement NDC target. Four scenarios are used to model the impact of carbon price up to 2050: no carbon price, Indonesia's current price of USD 2.02/tCO2e, ICPF middle- and high-income countries, USD 50/tCO2e and USD 75/tCO2e. Four price scenarios—10, 25, 35, and 150 USD/tCO2e —are added to better understand the carbon price's effects. As carbon prices rise, installed capacity and power generation will shift to lower-carbon technology. Ultracritical coal, gas-fired, solar, geothermal, and hydropower plants will replace subcritical coal. Investment, fixed, and variable costs would exceed BaU with a higher carbon price. 2.02 to 25 USD/tCO2e can start the coal-to-gas switch but not significantly change the generation profile. The generation will change significantly above 35 USD/tCO2e. Carbon emissions peak lower with rising carbon prices. USD 25 carbon price reduces emissions significantly; a carbon price below that is costly and ineffective. Indonesian Law No. 16 of 2016 ratified the Paris Agreement NDC, committing Indonesia to reduce greenhouse gas emissions by 29% by 2030 or 41% with international assistance. Energy sector emissions need to decrease by 11% for a 29 percent reduction and 14% for a 41 percent reduction. A 29% reduction requires USD 39.65/tCO2e carbon price, while a 41% reduction requires USD 43.78/tCO2e. These prices are still within the reasonable ICPF price limit for Indonesia to approach the middle-income country price floor. [ABSTRACT FROM AUTHOR]

Accurate and stable carbon price forecasts serve as a reference for assessing the stability of the carbon market and play a vital role in enhancing investment and operational decisions. However, realizing this goal is still a significant challenge, and researchers usually ignore multi-step-ahead and interval forecasting due to the non-linear and non-stationary characteristics of carbon price series and its complex fluctuation features. In this study, a novel hybrid model for accurately predicting carbon prices is proposed. The proposed model combines multi-step-ahead and interval carbon price forecasting based on the Hampel identifier (HI), time-varying filtering-based empirical mode decomposition (TVFEMD), and transformer model. First, HI identifies and corrects outliers in carbon price. Second, TVFEMD decomposes carbon price into several intrinsic mode functions (imfs) to reduce the non-linear and non-stationarity of carbon price to obtain more regular features in series. Next, these imfs are reconstructed by sample entropy (SE). Subsequently, the orthogonal array tuning method is used to optimize the transformer model's hyperparameters to obtain the optimal model structure. Finally, after hyperparameter optimization and quantile loss function, the transformer is used to perform multi-step-ahead and interval forecasting on each part of the reconstruction, and the final prediction result is obtained by summing them up. Five pilot carbon trading markets in China were selected as experimental objects to verify the proposed model's prediction performance. Various benchmark models and evaluation indicators were selected for comparison and analysis. Experimental results show that the proposed HI-TVFEMD-transformer hybrid model achieves an average MAE of 0.6546, 1.3992, 1.6287, and 2.2601 for one-step, three-step, five-step, and ten-step-ahead forecasting, respectively, which significantly outperforms other models. Furthermore, interval forecasts almost always have a PICI above 0.95 at a confidence interval of 0.1, thereby indicating the effectiveness of the hybrid model in describing the uncertainty in the forecasts. Therefore, the proposed hybrid model is a reliable carbon price forecasting tool that can provide a dependable reference for policymakers and investors. [ABSTRACT FROM AUTHOR]

Methods of computational intelligence show a high potential for short-term price forecasting of the energy market as they offer the possibility to cope with the complexity, multi-parameter dependency, and non-linearity of pricing mechanisms. While there is a large number of publications applying neural networks to the prediction of electricity prices, the analysis of natural gas and carbon prices remains scarce. Identifying a best practice from the literature, this study presents an iterative approach to optimize both the input values and network configuration of neural networks. We apply the approach to the natural gas and carbon market, sequentially testing autoregressive and exogenous explanatory variables as well as different neural network architectures. We subsequently discuss the influence of architectural properties, input parameters, data preparation, and the models' resilience to singular events. Results show that the selection of appropriate lags of gas and carbon prices to account for autoregressive properties of the respective time series leads to a high degree of forecasting accuracy. Additionally, including ambient temperature data can slightly reduce errors of natural gas price forecasting whereas carbon price predictions benefit from electricity prices as a further explanatory input. The best configurations presented in this contribution achieve a root mean square error (RMSE) of 0.64 EUR/MWh (natural gas prices) corresponding to a normalized RMSE of 0.037 and 0.33 EUR/tCO2 (carbon prices) corresponding to a normalized RMSE of 0.023. [ABSTRACT FROM AUTHOR]

We formulate and compare optimization models of investment in renewable generation using a suite of social planning models that compute optimal generation capacity investments for a hydro-dominated electricity system where inflow uncertainty results in a risk of energy shortage. The models optimize the expected cost of capacity expansion and operation allowing for investments in hydro, geothermal, solar, wind, and thermal plant, as well as battery storage for smoothing load profiles. A novel feature is the integration of uncertain seasonal hydroelectric energy supply and short-term variability in renewable supply in a two-stage stochastic programming framework. The models are applied to data from the New Zealand electricity system and used to estimate the costs of moving to a 100% renewable electricity system by 2035. We also explore the outcomes obtained when applying different forms of CO2 constraint that limit respectively non-renewable capacity, non-renewable generation, and CO2 emissions on average, almost surely, or in a chance-constrained setting, and show how our models can be used to investigate the merits of a proposed pumped-hydro scheme in New Zealand's South Island. [ABSTRACT FROM AUTHOR]

Research on climate change mitigation has increasingly considered carbon pricing, with these efforts concentrating on reductions in carbon dioxide (CO2) emissions. Our comprehensive cross-country analysis extends this focus by quantitatively evaluating the effects of carbon pricing on four major pollutants: CO2, nitrous oxide (N2O), methane (CH4), and particulate matter (PM). We use regressions and introduce entropy balancing to this research area. Analyzing data from 132 countries from 1992 to 2019, we find that carbon pricing is associated with an average annual reduction in CO2 emissions by 3 percentage points. A one-unit increase in a coverage-weighted carbon price is associated with reductions in N2O emissions by approximately 0.1 percentage points. A shorter panel for 2010–2017 shows a larger impact of 0.3 percentage points for PM. These findings underline the efficacy of carbon pricing not just in curtailing CO2 but in significantly mitigating other harmful pollutants on a global scale. Reductions in pollutants beyond CO2 provide further motivation for policymakers to pursue carbon pricing.

The high overlap of participants in the carbon emissions trading and electricity markets couples the operations of the two markets. The carbon emission cost (CEC) of coal-fired units becomes part of the power generation cost through market coupling. The accuracy of CEC calculation affects the clearing capacity of coal-fired units in the electric power market. Study of carbon-electricity market interaction and CEC calculations is still in its initial stages. This study analyzes the impact of carbon emissions trading and compliance on the operation of the electric power market and defines the cost transmission mode between the carbon emissions trading and electric power markets. A long-period interactive operation simulation mechanism for the carbon-electricity market is established, and operation and trading models of the carbon emissions trading market and electric power market are established. A daily rolling estimation method for the CEC of coalfired units is proposed, along with the CEC per unit electric quantity of the coal-fired units. The feasibility and effectiveness of the proposed method are verified through an example simulation, and the factors influencing the CEC are analyzed. [ABSTRACT FROM AUTHOR]

Extreme weather is an unexpected shock to the socioeconomic, which is likely to create climate risks in the process of global warming mitigation. The aim of this study is to investigate the impact of extreme weather on prices of China's regional emission allowances, by using the panel data of four representative pilots in China (Beijing, Guangdong, Hubei, and Shanghai) from April 2014 to December 2020. The overall findings reveal that extreme weather, especially extreme heat, has a short-term lagged positive impact on carbon prices. In particular, the specific performance of extreme weather under different conditions is as follows: (i) carbon prices in tertiary-dominated markets are more sensitive to extreme weather, (ii) extreme heat has a positive effect on carbon prices while extreme cold does not, and (iii) the positive impact of extreme weather on carbon market is significantly stronger during compliance periods. This study provides the decision-making basis for emission traders to avoid losses caused by market fluctuations. [ABSTRACT FROM AUTHOR]

Under the efficient market hypothesis (EMH), part of the literature ignores the characteristics of carbon markets. Based on the fractal market hypothesis (FMH), using the MF-DCCA method, this empirical study complements related research. We studied the non-linear multifractal correlation between carbon price fluctuations and China's economic policy uncertainty (CNEPU) in Shenzhen, Beijing, Tianjin, and the national carbon market. The results show the following: (1) There is no linear correlation between price volatility and CNEPU in all carbon markets. (2) In the national carbon market, the correlation linkage between price fluctuation and CNEPU has not yet formed. (3) In the three regional carbon markets of Shenzhen, Beijing, and Tianjin, the long-range correlations exist with anti-persistence multifractal characteristics, which means that an increase in CNEPU will reduce price fluctuations. (4) After dividing the time scale into long-term and short-term, we found that it does not change the multifractal characteristics but it does change the fractal intensity. Finally, some suggestions are given to policymakers and carbon finance investors. [ABSTRACT FROM AUTHOR]

The high volatility and uncertainty of carbon price have always been two major challenges in carbon price forecasting. To solve these two challenges, an adaptive lower‐and upper‐bound estimation (LUBE) model with improved variational mode decomposition (VMD) and PSO‐based interval optimization strategy is proposed for interval prediction of carbon price. To validate effectiveness and superiority, the adaptive LUBE model and several competitive models, including the bootstrap model, delta model, and Bayesian model, were utilized for interval prediction of carbon prices of Beijing and Shanghai. Compared with other models, the adaptive LUBE model not only has excellent coverage but also has the narrowest interval width in both training set and test set. Therefore, the excellent comparison results show that the proposed model can obtain a more reliable and higher‐quality prediction interval, which can be a novel and effective carbon prices forecasting tool for governments and enterprises. [ABSTRACT FROM AUTHOR]