Search

This paper introduces an innovative cooperative model for energy efficiency retrofitting that incorporates carbon emission rights, addressing critical financial constraints in Energy Performance Contracting (EPC). By employing the fuzzy analytic hierarchy process (F-AHP) to evaluate risk assessment indicators and stakeholder contributions and utilizing the enhanced Shapley method for equitable benefit distribution, the model demonstrates significant improvements in financing and efficiency for energy conservation projects. The findings are as follows: (1) the energy efficiency retrofit model, which integrates carbon emission rights, effectively alleviates the financial constraints and fosters energy conservation and emission reduction in guaranteed-savings EPC projects; (2) the enhanced Shapley method is deemed appropriate for the equitable distribution of energy-saving benefits among stakeholders; (3) when compared with the traditional model and the benefit allocation-absent carbon rights, the energy-saving benefits of the energy efficiency retrofit model incorporating carbon emission rights are higher in individual and overall terms. The findings of this study offer a viable solution to financing challenges faced by stakeholders in such projects and delineate a pragmatic approach for enterprises to enhance energy efficiency and reduce emissions. [ABSTRACT FROM AUTHOR]

The launch of the national carbon emissions trading (CET) market has resulted in a closer relationship between China's CET market and its electricity market, making it easy for risks to transfer between markets. This paper utilizes data from China's CET market and electric power companies between 2017 and 2023 to construct the spillover index model of Diebold and Yilmaz, the frequency-domain spillover approach developed by Barun'ik and Křehl'ik, and a minimum spanning tree model. The comparison is made before and after the launch of the national CET market. Subsequently, this paper examines the market spillover effects, as well as the static and dynamic properties of network structures, considering both the time domain and frequency-domain perspectives. The research findings suggest the following: (1) There is a strong risk spillover effect between China's CET market and the stock prices of electric power companies; (2) There is asymmetry in the paired spillover effects between carbon trading pilot markets and the national CET market, and differences exist in the impact of risk spillovers from power companies between the two; (3) The results of the MST model indicate that the risk contagion efficiency is higher in the regional CET pilot stage compared to the national CET market launch stage, with significant changes occurring in key nodes before and after the launch of the national CET market; (4) Both the dynamic spillover index and the standardized tree length results demonstrate that crisis events can worsen the risk contagion between markets. Besides offering a theoretical foundation and empirical evidence for the development of China's CET and electricity markets, the findings of this paper can provide recommendations for financial market participants as well. [ABSTRACT FROM AUTHOR]

With the proposal of China's "carbon peak, carbon neutral" strategy, the increasing awareness of low carbon production among consumers, and the government's introduction of carbon trading mechanism and low carbon consumption subsidy policies, enterprises are facing good opportunities for development. However, how the government can reasonably formulate low carbon policies and how enterprises can implement optimal low-carbon production decisions are still key issues in China's low-carbon transition development. In this context, this paper is based on the carbon trading mechanism and carbon consumption subsidies. In this context, based on the carbon trading mechanism, this paper focuses on green production and green consumption, considers the impact of low-carbon consumer preferences and government subsidies on enterprises' low-carbon production decisions, and uses the optimal theory to study the optimal pricing strategy and the optimal carbon reduction strategy. The study shows that the increase in carbon price has a positive effect on the increase in enterprise profit; the increase of carbon emission has a negative effect on the increase of enterprise profit and the high carbon price will intensify this effect. In addition, changing the intensity of government subsidies to consumers will lead to the change of enterprise carbon emission strategy. The study of this paper provides a certain reference for the government to reasonably formulate carbon trading prices and consumer low-carbon subsidies. In addition, considering consumer low-carbon preferences is also conducive to promoting green production practices of enterprises, thus promoting the realization of the carbon neutral strategy. [ABSTRACT FROM AUTHOR]

Coal-to-oil (CTL) combined with carbon capture, utilization and storage (CCUS) can significantly reduce the CO2 emissions generated in the production process to achieve clean coal utilization. Taking CTL enterprises as sources and deep saline aquifers and oil fields as sinks, this paper establishes a source–sink matching model, which is combined with a trinomial tree real-option model of carbon price fluctuation, and evaluates the investment decisions of CTL. The results show that 36 pipelines with an average transportation distance of 319.13 km and predominantly small diameters must be constructed for CO2 capture and storage combined with enhanced oil recovery (EOR). Under the current carbon price, 83.33% of enterprises can invest immediately when adopting EOR; when utilizing storage in a deep saline aquifer (DSF), even with a 50% subsidy and a decrease in costs due to the learning rate, enterprises still need to execute the deferred option investment. Government subsidies and technological advances can greatly increase the value of investment. The critical carbon price of CTL-CCUS projects is sensitive to government subsidies, technological advances, and CO2 transportation distances. Therefore, China should reasonably guide the development of the carbon market and give play to the role of the carbon market in emission reduction incentives. In addition, the Chinese government can provide direct financial support for the CTL-CCUS project to increase the enthusiasm of CTL enterprises for CCUS transformation and promote technological progress. [ABSTRACT FROM AUTHOR]

To combat climate change, the Chinese government has implemented a package of policies to support the development of the biogas power generation industry. However, the promotion of biogas power generation technology in China is relatively slow. Therefore, it is of practical significance to study the promotion of biogas power generation technology against the background of policy support. In order to study the effect of policy on the promotion of biogas power generation technology, a system dynamics model is constructed in this paper. The results show that under the feed-in tariff subsidy policy, biogas power generation technology can be well promoted because it has good economic and environmental effects. In addition, if the biogas power generation technology is considered to participate in carbon emission trading, the carbon price also has a positive impact on the promotion of biogas power generation technology because it increases the perceived economic value of biogas power generation projects. Finally, this study can also provide reference value for the promotion of biogas power generation technology in other areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the context of tax sharing reform and land reform during the 1990s, local governments in China relied heavily on land finance. Local governments have fierce competition in attracting investment, omitting the development of green economy. Based on the data of industrial land sales and carbon dioxide emissions, this study constructed the panel data of 196 cities in China from 2007 to 2017 and analyzed the spatial and temporal evolution characteristics of urban industrial land price distortion and carbon emission intensity. Furthermore, a multiple linear regression model was constructed from the aspects of scale effect to empirically analyze the overall impact and differential impact of urban industrial land price distortion on carbon emission intensity. With the help of the mediating effect model, the mechanism of urban industrial land price distortion on carbon emission intensity was investigated from the perspective of industrial structure upgrading. The results showed that (1) the higher the price distortion of industrial land is, the more detrimental it is to the development of green and low-carbon economy. (2) The regional heterogeneity test showed that the impact of industrial land price distortion on carbon emission intensity is most significant in the central region, medium cities, and cities with low fiscal self-sufficiency rates, respectively. (3) The higher the distortion degree of industrial land price, the greater the restriction on the upgrading of industrial structure, further increasing the carbon emission intensity. This paper provides policy implications for the market-oriented reform of land factors and the realization of the "double carbon" goal. [ABSTRACT FROM AUTHOR]

The exploration of the dependency structure of the Chinese and EU carbon trading markets is crucial to the construction of a globally harmonized carbon market. In this paper, we studied the characteristics of structural interdependency between China's major carbon markets and the European Union (EU) carbon market before and after the launch of the national carbon emissions trading scheme (ETS) and the occurrence of the new coronavirus (COVID-19) by applying the C-vine copula method, with the carbon trading prices of the EU, Beijing, Shanghai, Guangdong, Shenzhen and Hubei as the research objects. The study shows that there exists a statistically significant dependence between the EU and the major carbon markets in China and their extremal dependences and dependence structures are different at different stages. After the launch of the national carbon ETS, China has become more independent in terms of interdependency with the EU carbon market, and is more relevant between domestic carbon markets. Most importantly, we found that the dependence between the EU and Chinese carbon markets has increased following the outbreak of COVID-19, and tail dependency structures existed before the launch of the national carbon ETS and during the outbreak of the COVID-19. The results of this study provide a basis for the understanding of the linkage characteristics of carbon trading prices between China and the EU at different stages, which in turn can help market regulators and investors to formulate investment decisions and policies. [ABSTRACT FROM AUTHOR]

China's green credit has mostly been invested in new energy areas with positive environmental externalities in recent years, while coal power enterprises have been neglected. This paper constructs a tripartite evolutionary game model among government, coal power enterprises, and banks to clarify the key factors and mechanisms for coal power enterprises undergoing green transformation. The research results show that: Firstly, to realize the spontaneous green transformation of coal power enterprises, spontaneous profitability must be achieved before the removal of policy incentives, which is reflected in the continuous increase in electricity price, carbon emission trading price, and decrease in green transformation cost. Secondly, the green credit adjustment factor cannot determine whether a company chooses to undertake green transition, but it provides a valuable window of green transition for companies. When the relative benefits of green transformation projects are greater than the relative costs, coal power enterprises will undergo green transformation spontaneously. Thirdly, lower green credit interest rates are not better. An optimal green credit interest rate exists, that allows coal power enterprises to obtain the longest transition window in which to achieve spontaneous profitability for green transition projects. [ABSTRACT FROM AUTHOR]

Accurately forecasting carbon prices is key to managing associated risks in the financial market for carbon. To this end, the traditional strategy does not adequately decompose carbon prices, and the kernel extreme learning machine (KELM) with a single kernel function struggles to adapt to the nonlinearity, nonstationarity, and multiple frequencies of regional carbon prices in China. This study constructs a model, called the VMD-ICEEMDAN-RE-SSA-HKELM model, to forecast regional carbon prices in China based on the idea of 'decomposition–reconstruction–integration'. The VMD is first used to decompose carbon prices and the ICEEMDAN is then used to decompose the residual term that contains complex information. To reduce the systematic error caused by increases in the mode components of carbon price, range entropy (RE) is used to reconstruct the results of its secondary decomposition. Following this, HKELM is optimized by the sparrow search algorithm and used to forecast each subseries of carbon prices. Finally, predictions of the price of carbon are obtained by linearly superimposing the results of the forecasts of each of its subseries. The results of experiments show that the secondary decomposition strategy proposed in this paper is superior to the traditional decomposition strategy, and the proposed model for forecasting carbon prices has significant advantages over a considered reference group of models. [ABSTRACT FROM AUTHOR]

The adoption of emissions trading scheme (ETS) and renewable energy sources (RES) policies have been essential to achieving China's national targets for reducing CO2 emissions and developing non-fossil energy sources. The combination of ETS and RES policies raises an important issue: What is the effect of combining ETS and RES policies on the existing carbon market and economy? Focusing on the design of the nationwide carbon market, this paper uses a multi-regional computable general equilibrium (CGE) model to analyze the economic impacts of ETS policy when combined with RES policies in China. The results show that China's annual ETS emissions cap should decrease by 0.3% to maintain stable CO2 prices and achieve the targets in China's intended nationally determined contribution (INDC). It is estimated that the CO2 price on the nationwide carbon market would decrease by 11-64% when the renewable energy subsidy rate increases from 20 to 100%, and the total trading volume would decrease by 3-25%. The results also show that the combination of an ETS and a feed-in tariff (FIT) results in greater GDP cost and welfare loss in all Chinese regions, increasing the total social cost by 0.01-0.06%. [ABSTRACT FROM AUTHOR]

The carbon emission trading markets represent an emerging domain within China. The primary objective of this study is to explore whether carbon price volatility influences stock market volatility among companies subject to these emission trading regulations. Employing daily returns data from 293 publicly traded companies regulated by these emission trading markets, this study encompasses the national carbon market and eight pilot regional carbon markets spanning from August 2013 to October 2023. The results demonstrate that volatility in regional carbon prices positively impacts the stock volatility of companies in the corresponding emission trading region, indicating a volatility spillover effect. Moreover, this spillover effect is more pronounced in sectors marked by lesser carbon intensity than those with greater carbon intensity. The volatility transmission is more pronounced in coastal areas than in inland regions. However, no notable distinctions in volatility transmission are discerned between the periods before and throughout the COVID-19 pandemic. Vector autoregression analyses substantiate that lagged carbon price fluctuations possess limited predictive capacity for contemporaneous equity market volatility and vice versa. The robustness of these outcomes is fortified by applying the E-GARCH model, which accounts for the volatility clustering phenomenon. As the first investigation into the volatility spillover effect between China's emission trading market and corresponding stock markets, this study offers valuable insights into the investment strategies of retail investors, the formulation of carbon regulations by policymakers, and the carbon emission strategies of corporate managers. [ABSTRACT FROM AUTHOR]

Inter-provincial power transmission in China solved the problem of electricity production and consumption spatial mismatch, which also facilitated Chinese green energy transition and sustainable development. Aiming to understand the spatial patterns and main driving factors, this paper accounted the aggregate carbon intensity per electricity produced by each province, built the inter-provincial Out–Destination matrixes of carbon emission embodied in power transmission among 27 provincial areas of 2006 and 2019, and quantitatively analyzed the change in spatial patterns of carbon flow and carbon intensity difference between power inward and outward provinces. We found that the inter-provincial power transmission has led to the reduction in the national average carbon intensity per electricity produced from 0.855 kg/kWh in 2006 to 0.628 kg/kWh in 2019 and the green energy transition of all provinces. The spatial morphology characteristics of carbon flow show the 'three corridors of West-East Power Transmission (WEPT)' pattern, in which power is transmitted from some main thermal power-dominated provinces in central and north-west China to the eastern coastal provinces. Further, it resulted in the carbon leakage from electricity consumption of the coastal provinces and carbon overload of the hinterland provinces. Despite natural resource endowment, the location of the provinces in the national power grid, the national low-carbon energy transition strategy, as well as advances in long-distance ultra-high-voltage power transmission technologies were the main factors to the formation and evolution of Chinese electricity green transition. [ABSTRACT FROM AUTHOR]

Under the strict carbon emission quota policy in China, the urban carbon price directly affects the operation of enterprises, as well as forest carbon sequestration. As a result, accurately forecasting carbon prices has been a popular research topic in forest science. Similar to stock prices, urban carbon prices are difficult to forecast using simple models with only historical prices. Fortunately, urban remote sensing images containing rich human economic activity information reflect the changing trend of carbon prices. However, properly integrating remote sensing data into carbon price forecasting has not yet been investigated. In this study, by introducing the powerful transformer paradigm, we propose a novel carbon price forecasting method, called MFTSformer, to uncover information from urban remote sensing and historical price data through the encoder–decoder framework. Moreover, a self-attention mechanism is used to capture the intrinsic characteristics of long-term price data. We conduct comparison experiments with four baselines, ablation experiments, and case studies in Guangzhou. The results show that MFTSformer reduces errors by up to 52.24%. Moreover, it outperforms the baselines in long-term accurate carbon price prediction (averaging 15.3%) with fewer training resources (it converges rapidly within 20 epochs). These findings suggest that the effective MFTSformer can offer new insights regarding AI to urban forest research. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent years, with the intensification of global warming and the greenhouse effect, the global consensus has focused on efficient, clean, low-carbon, and green development as a means of achieving new economic growth. China, as a major carbon emitter, has been at the forefront of efforts to reduce carbon emissions. The establishment of the carbon emissions trading market, commonly known as the "carbon market", provides an economic solution for reducing carbon emissions in both the carbon and energy markets. As China's carbon market continues to grow rapidly, fluctuations in the energy or carbon markets caused by information shocks can easily spread between the two markets, leading to increased interconnectedness. Moreover, the spillover effect of the volatility between China's carbon market and energy market is not constant, and the intensity and direction of this effect vary depending on different market volatility levels and periods. Therefore, it is crucial to conduct a comprehensive study on the characteristics of the volatility spillover effect between China's carbon market and energy market and to fully understand the mechanism of energy regulation on carbon prices. This research will have significant practical implications for promoting the establishment of a well-functioning internal price transmission mechanism between China's carbon market and energy market. This study took the risk spillover between the carbon market and energy market as the research object and systematically combed through its pricing mechanism and spillover impact. Through constructing the DY overflow index model based on a VAR model and generalized variance decomposition method, this study explored the linkage between China's carbon and energy markets, i.e., the linkage of price fluctuations between China's energy and carbon markets, as well as the time-varying nature of inter-market spillovers, and provides suggestions on the risk control of price fluctuations between the carbon and energy markets. [ABSTRACT FROM AUTHOR]

Recently, carbon price forecasting has become critical for financial markets and environmental protection. Due to their dynamic, nonlinear, and high noise characteristics, predicting carbon prices is difficult. Machine learning forecasting often uses stacked ensemble algorithms. As a result, common stacking has many limitations when applied to time series data, as its cross-validation process disrupts the temporal sequentiality of the data. Using a double sliding window scheme, we proposed an improved stacking ensemble algorithm that avoided overfitting risks and maintained temporal sequentiality. We replaced cross-validation with walk-forward validation. Our empirical experiment involved the design of two dynamic forecasting frameworks utilizing the improved algorithm. This incorporated forecasting models from different domains as base learners. We used three popular machine learning models as the meta-model to integrate the predictions of each base learner, further narrowing the gap between the final predictions and the observations. The empirical part of this study used the return of carbon prices from the Shenzhen carbon market in China as the prediction target. This verified the enhanced accuracy of the modified stacking algorithm through the use of five statistical metrics and the model confidence set (MCS). Furthermore, we constructed a portfolio to examine the practical usefulness of the improved stacking algorithm. Empirical results showed that the improved stacking algorithm could significantly and robustly improve model prediction accuracy. Support vector machines (SVR) aggregated results better than the other two meta-models (Random forest and XGBoost) in the aggregation step. In different volatility states, the modified stacking algorithm performed differently. We also found that aggressive investment strategies can help investors achieve higher investment returns with carbon option assets. [ABSTRACT FROM AUTHOR]

Effective prediction of carbon prices matters a great deal for risk management in the carbon financial market. This article designs a blended approach incorporating secondary decomposition and nonlinear error-correction technology to predict the regional carbon price in China. Firstly, the variational mode decomposition (VMD) method is used to decompose the carbon price, and then, the time-varying filter-based empirical mode decomposition (TVFEMD) is introduced to decompose the residual term generated by VMD, and the multiple kernel-based extreme learning machine (MKELM) optimized by the sparrow search algorithm (SSA) is innovatively built to forecast the carbon subsequences. Finally, in order to mine the hidden information contained in the forecasted error, the nonlinear error-correction method based on the SSA-MKELM model is introduced to correct the initial prediction of carbon price. The empirical results show that the proposed model improves the prediction accuracy of carbon prices, with RMSE, MAE, MAPE, and DS up to 0.1363, 0.1160, 0.0015, and 0.9231 in Guangdong, respectively. In the case of the Hubei market, the model also performs best. This research innovatively expands the prediction theory and method of China's regional carbon price. [ABSTRACT FROM AUTHOR]

The scientific judgement of the structural abrupt transition characteristics of the carbon market price is an important means to comprehensively analyze its fluctuation law and effectively prevent carbon market risks. However, the existing methods for identifying structural changes of the carbon market based on carbon price data mostly regard the carbon price series as a deterministic time series and pay less attention to the uncertainty implied by the carbon price series. We propose a framework for identifying abrupt transitions in the carbon market from the perspective of a complex network by considering the influence of random factors on the carbon price series, expressing the carbon price series as a sequence of probability density functions, using the distribution of probability density to reveal the uncertainty information implied by carbon price series and constructing a recurrence network of carbon price probability density. Based on the community structure, the break index and statistical test method are defined. The simulation verifies the effectiveness and superiority of the method compared with traditional methods. An empirical analysis uses the carbon price data of the European Union carbon market and seven pilot carbon markets in China. The results show many abrupt transitions in the carbon price series of the two markets, whose occurrence period is closely related to major events. [ABSTRACT FROM AUTHOR]

Carbon capture and storage (CCS) technology is an attractive technique to help power enterprises with carbon emission reduction. In this paper, a two-stage CCS retrofit investment in an existing coal-fired power plant in China including the first stage (demonstration project) and second stage (commercial operation) is taken as a case to decide when and whether to invest. Distinguished from previous models, a binomial lattice compound real options model including the options to defer and expand is established. Further, the accounting approaches to certified emission reductions (CERs) based on the thermodynamics principle are first proposed concerning this model. We find the total invest value under compound options model is less than zero, although greater than that by NPV method. The results indicate carbon prices and subsidy policy, respectively, play a dominating role in initiating the CCS investment at the first and second stage. The growth in government subsidy at the first stage has obviously greater effects on decreasing critical carbon trading prices. Besides, the minimum critical carbon price is 87.09 RMB/ton with full subsidy, greater than the current price (56 RMB/ton). This also illustrates it is not the optimal occasion to invest in a CCS retrofit project for power enterprises. [ABSTRACT FROM AUTHOR]

Carbon price forecasting is significant to both policy makers and market participants. However, since the complex characteristics of carbon prices are affected by many factors, it may be hard for a single prediction model to obtain high-precision results. As a consequence, a new hybrid model based on multi-resolution singular value decomposition (MRSVD) and the extreme learning machine (ELM) optimized by moth–flame optimization (MFO) is proposed for carbon price prediction. First, through the augmented Dickey–Fuller test (ADF), cointegration test and Granger causality test, the external factors of the carbon price, which includes energy and economic factors, are selected in turn. To select the internal factors of the carbon price, the carbon price series are decomposed by MRSVD, and the lags are determined by partial autocorrelation function (PACF). MFO is then used for the optimization of ELM parameters, and external and internal factors are input to the MFO-ELM. Finally, to test the capability and effectiveness of the proposed model, MRSVD-MFO-ELM and its comparison models are used for carbon price forecast in the European Union (EU) and China, respectively. The results show that the performance of the model is significantly better than other models. [ABSTRACT FROM AUTHOR]

This study investigated the potential financial benefits that private forest famers can derive when participating in a larch carbon sink plantation project in the northwestern Chinese province of Gansu. A decision matrix was developed to help forest farmers justify participation in forest carbon sink projects relative to the traditional land-use goal of timber production under various carbon trading prices, site conditions, and contract terms. The results showed that when the carbon trading price is at the theoretically optimal carbon price (CNY 110/tCO2e, equivalent to USD 17/tCO2e), Chinese business entities are willing to pay for forest carbon credits under the current global carbon emissions level, and forest farmers who participate in a 25-year forest carbon sink plantation project on high-productivity sites would generate the greatest financial benefit compared with the net income from pure timber production forests. Thus, the government does not need to provide a carbon sink subsidy for participating tree farmers. However, at the current average carbon trading price (CNY 19.8/tCO2e or USD 3/tCO2e) in the domestic market, a minimum additional subsidy of CNY 735/ha (USD 113/ha) is required upfront to motivate forest farmers to convert timber production forests into carbon sink forests. The results of this study can help policymakers and forest managers formulate optimal eco-compensation strategies for enrollment in forest-based carbon sequestration programs. [ABSTRACT FROM AUTHOR]

Emissions trading schemes (ETSs) are effective measures that facilitate economic growth and carbon mitigation, especially for developing countries such as China. These schemes can further affect the cash flow, production, and investment decisions of regulated companies. However, few empirical studies have explored how ETSs promote companies' market value. We systematically evaluate the influence of the carbon emission trading (CET) policy on companies' market value and explore the influential mechanism. We use the data of listed companies from the Chinese stock "A" markets and employ the difference-in-difference method to account for the unobserved cause of the CET policy regarding companies' market value. Robust benchmark regression results reveal that the CET policy promotes companies' market value significantly. The mechanism analysis reveals that the CET policy can improve the market value of listed companies by influencing the carbon price, innovative activities, and carbon disclosure. The results of the heterogeneity analysis show that the CET policy's impact on companies' market value is heterogeneous in terms of marketization degree, industry, firm ownership, and different regions. We suggest that the carbon pricing mechanism, degree of market perfection, carbon disclosure policy, and carbon finance should be optimized to improve the efficiency of ETSs. [ABSTRACT FROM AUTHOR]

Carbon leakage features prominently in the climate policy debate in economies implementing climate policies, especially in the EU. The imposition of carbon pricing impacts negatively the competitiveness of energy-intensive industries, inducing their relocation to countries with weaker environmental regulation. Unilateral climate policy may complement domestic emissions pricing with border carbon adjustment to reduce leakage and protect the competitiveness of domestic manufacturing. Here, we use an enhanced version of GEM-E3-FIT model to assess the macro-economic impacts when the EU unilaterally implements the EU Green Deal goals, leading to a leakage of 25% over 2020–2050. The size and composition, in terms of GHG and energy intensities, of the countries undertaking emission reductions matter for carbon leakage, which is significantly reduced when China joins the mitigation effort, as a result of its large market size and the high carbon intensity of its production. Chemicals and metals face the stronger risks for relocation to non-abating countries. The Border Carbon Adjustment can largely reduce leakage and the negative activity impacts on energy-intensive and trade-exposed industries of regulating countries, by shifting the emission reduction to non-abating countries through implicit changes in product prices. [ABSTRACT FROM AUTHOR]