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The mitigation of climate change requires that agricultural development would proceed in accordance with green and sustainable practices. This implies that economic performance should be improved while minimizing the impact on the environment. One way to assess the underlying costs of sustainable agricultural development, and to model the relationship between the environment and the economy in general, is to use the carbon shadow price (CSP). In this paper, we utilize a by-production model within a non-parametric framework to estimate the agricultural CSP from 1997 to 2020 in 31 provinces of China. The patterns of the agricultural CSP are unveiled through kernel density analysis and convergence tests. Additionally, this paper constructs a comprehensive agricultural informatization indicator using the entropy method. We then empirically investigate its influence on agricultural CSP. The results reveal that China's agricultural CSP has demonstrated consistent growth over the past two decades which suggests serious improvement in environmental performance. Furthermore, the CSP show cross-province variation, albeit with conditional β convergence. Our empirical findings indicate that agricultural information technology contributes to an increase in CSP, thereby indicating its positive role on the green growth of agriculture in China. [ABSTRACT FROM AUTHOR]

Combined heat and power is an inevitable trend in the development of waste-to-power industry. Many waste incineration power generation projects that have already put into operation in China are waiting for transition and upgrading to combined heat and power projects. From a growth option perspective, this paper considers the irreversible process and uncertainty in economic benefits, introduces carbon emission trading mechanism and develops the growth option theory to design the expansion decision making model of waste-to-energy combined heat and power projects, so as to obtain the project value and find the optimal expansion timing. In this paper, a case study is carried out on the expansion decision making of a waste-to-energy combined heat and power project in Shandong Province in China. And two scenarios with specific and flexible expansion times are developed from European and American options separately, as European options require investors to expand at a certain time but American options not. The results indicate that 1) real option approach successfully improves the project value through considering uncertainty, and American option can achieve the optimal expansion time, 2) under the carbon emission trading mechanism, the carbon price affects the expansion index, which has a positive effect on the project value, 3) the linear nature of the income and cost expansion index can help investors make judgments on expansion investments. The effectiveness of the case study implies the practicality of the expansion option method for similar projects to reach dual carbon targets in cost and benefit analysis. • Real option approach is applied in energy transition in WTECHP projects. • European and American options are proposed for certain and flexible decision times. • Expansion time and triggers of WTECHP are incorporated into life cycle management. • CCER is introduced as an environmental element to evaluate expansion decision making. [ABSTRACT FROM AUTHOR]

Carbon trading market is a central policy tool for promoting cleaner enterprises' energy consumption and investment behaviors. It is crucial for enterprises to establish a green and low-carbon development model by clarifying carbon trading impact mechanism on enterprise behaviors and the interactive relationship between entities in the carbon market. China's contract-based carbon market is still in the development stage, the impact of the mainstream allowance mechanism and government regulation on enterprises' energy behaviors has yet to be determined. In view of this, this paper describes mathematical mechanisms of the influence of carbon prices and carbon quotas on enterprises' energy consumption and investment, and the evolutionary game model of government-enterprise behaviors is constructed, the evolution process and appropriate strategies of enterprises' energy behaviors in the development of the carbon market are discussed. This paper uses real data of China carbon market to simulate the model, it is found that strategies such as relaxation of carbon quotas and carbon subsidies dynamization are needed during the initial development of the market. In the mature stage of the carbon market, strategies are needed to increase the proportion of government carbon revenue and reduce its regulatory cost. The higher the initial willingness of government-enterprise, the better the effect of strategies and the constraints of public willingness. The model and results have theoretical implications for enterprises' energy transformation and government regulation, and provide a reference for the development of China's carbon market. • Constructed benefit-cost analytical framework for enterprises' energy behaviors. • Described mechanisms of carbon trading affecting enterprises' energy behaviors. • Constructed government-enterprise behaviors model by the evolutionary game theory. • Found new conclusions of enterprises' energy behaviors under the carbon market. [ABSTRACT FROM AUTHOR]

As global warming intensifies, the reduction of carbon emissions is imminent. Carbon price is directly related to whether carbon can be effectively reduced. Therefore, accurately forecasting carbon price has important practical significance. Aiming at the nonstationary and nonlinear characteristics of carbon price, this paper proposes a novel hybrid model for forecasting carbon price, which is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multiscale fuzzy entropy (MFE), complete ensemble empirical mode decomposition (CEEMD), improved random forest by salp swarm algorithm (SSARF), improved back propagation by cuckoo search (CSBP), improved extreme learning machine by whale optimization algorithm (WOAELM) and error correction (EC), named ICEEMDAN-MFE-CEEMD-SSARF-CSBP-WOAELM-EC. Firstly, carbon price is decomposed by ICEEMDAN, divided into high-, medium-, and low-complexity components by MFE. Secondly, high-complexity components are merged and secondarily decomposed by CEEMD, which are still recorded as high-complexity components. Then, SSARF, CSBP and WOAELM are used to forecast high-, medium-, and low-complexity components, respectively, and forecasting results are reconstructed. Finally, EC is carried out using an extreme learning machine to obtain the final forecasting results, and the Diebold-Mariano test is introduced for a comprehensive evaluation of the model. Taking carbon price in the pilot cities of Shenzhen and Hubei as examples, after 6 aspects and 20 comparative experiments, the results show that the proposed model has higher forecast accuracy, with MAPE, MAE and RMSE up to 0.03131, 0.00089 and 4.02e-06 in Hubei, and its forecasting ability is better than other commonly used international carbon financial price forecasting models, providing a theoretical and data basis for carbon pricing and formulating carbon reduction policies in China. The main contributions of this paper are the improved primary decomposition, the use of secondary decomposition, and the innovative combination of three optimal models to forecast carbon price, but it still needs to be optimized for practice. [Display omitted] • Propose improve CEEMDAN by using Hermite interpolation and calculating residuals first. • High complexity components are secondarily decomposed to reduce its complexity. • Improved RF by SSA, named SSARF, is proposed to solve the dilemmas of RF. • Propose a high-accuracy hybrid model for carbon price forecasting. • Compare multi-faceted experiments to prove the validity of the proposed model. [ABSTRACT FROM AUTHOR]

In order to probe collaborative environmental governance (CEG) in China's "1 + N" policy framework for carbon peak and carbon neutrality, this paper applies the Actor-network theory (ANT) as an analytical framework to explore carbon reduction policy coordination. We then model ANT's relations with an evolutionary game theory (EGT) model to examine the evolutionarily stable strategies in CO 2 emission reduction by various actors including the central government, local governments, commercial banks, and polluting enterprises. Our objective is to examine policy coordination as the key enabler for governing success via an ANT-based mechanism design in China's institutional context. Combining the strength of ANT with a four-party EGT model, we investigate how to improve policy coordination to mobilize the actor-network for promoting CEG. Simulations are conducted to examine the effectiveness of policy coordination involving multiple policy instruments for controlling carbon emissions. The central government fulfills leadership in translating the interests of other actors and shaping the desired collaborative network. Without proper coordination, environmental policies may counteract each other. Government transfers and subsidies have direct impacts, but in the long run enhanced competitiveness and profitability of enterprises represent the intrinsic key to CEG success. From a methodology perspective, policy simulation in an EGT framework represents a novel addition to the research toolbox that expands ANT. [ABSTRACT FROM AUTHOR]

Under the background that China will achieve the goal of carbon neutrality by 2060, analyzing the impact of carbon neutral target constraints on the welfare of urban and rural residents is an important part of the design of a carbon emission reduction policy mechanism, in this paper, the dynamic recursive CGE(Computable general equilibrium) model is used to simulate the changes in welfare level, income consumption, and CO2 emissions of residents and their urban-rural heterogeneity under the policy of imposing a carbon tax on the fossil energy industry from 2022 to 2060. The results show that: compared with the BAU scenario, by 2060, first the income and consumption of residents will decrease slightly. Second, the target constraint of carbon neutrality makes the equivalent variation and compensate variation of urban and rural residents' welfare decrease slightly, and the equivalent variation and compensate variation of urban residents decrease more than those of rural residents. The following conclusions are drawn: first, the carbon-neutral target constraint makes the welfare of urban and rural residents decline slightly, but it will not have a strong impact on residents' income and consumption. Second, this constraint has a greater impact on the income, consumption and savings of urban residents than those of rural residents. [Display omitted] • A five-layer nested dynamic recursive CGE model is established to analyze residents' welfare. • Carbon taxes are incorporated into production decisions as a policy cost for energy companies. • The heterogeneity of the impact of carbon tax policy constraints on the welfare of urban and rural residents was analyzed. • The government needs to improve the intensive and diversified energy supply in rural areas to reduce the urban-rural gap. [ABSTRACT FROM AUTHOR]

Towards the target of carbon neutrality, the need for deep decarbonization in iron and steel industry is increasingly recognized. Existing studies rarely mention the effects of collaboration with its source industries for carbon emission reduction in ISI. Furthermore, the fundamental laws of CO 2 emission reduction capacity with cost change of source substitutions for various steel production routes under market economics are unclear. To avoid the blind substitution of clean sources for meeting the urgent requirements for decarbonization in China's steel industry, which may result in increased carbon emissions and uneconomical carbon reduction from a lifecycle perspective. This paper integrates the production and utilization processes of source resources into the steel production process, establishes coupling models of carbon and value flows for four main types of steel production routes, and aims to quantitatively analyze the specific CO 2 emission reduction potential and costs. Three economic market development scenarios from 2021 to 2060 are set, and the marginal carbon prices of various source substitutions are determined. Subsequently, decarbonization pathways with the goal of minimizing production costs are developed. Finally. The future resource demand and the carbon emission reduction contributions of source industries collaborated are predicted. The results show that (1) about 81.7–89.9 % of CO 2 emissions could be reduced in the scenarios; (2) the demands of electric power and hydrogen energy will reach 54.48–511.90 BkWh and 7.47–10.31 Gt in 2060, respectively; and (3) collaboration with source industries could make 18.3–18.3% of CO 2 emission reduction contributions. All in all, this study could provide scientific support for the low-carbon transformation of China's steel industry. • Carbon and value flows of BF-BOF, Scrap-EAF, DRI-EAF, MOE-EAF routes are analyzed. • CO 2 emission reduction potential with marginal carbon price is determined. • Decarbonization paths during 2021–2060 for China's steel industry are developed. • Quantify the contribution of collaborative carbon reduction with source industries. [ABSTRACT FROM AUTHOR]

Electric arc furnace (EAF) steelmaking process is an environmentally friendly, energy saving, and low-carbon route to produce steel, but its further development is restricted by extensive electricity consumption in China. In this paper, the EAF steelmaking processes driven by solar energy system (EAF-SES) was developed, which supplies electricity for the whole process, and reduce the dependence on traditional energy sources. Life cycle assessment and techno-economic analysis of EAF steelmaking processes integrated with traditional energy system (EAF-TES) and EAF-SES was conducted according to the data of 1536 heats based on the function unit of 1000 kg of molten steel (MS). EAF-SES and EAF-TES were studied and compared from perspectives of life cycle carbon footprint (LCCF), production cost (PC), raw materials cost (RMC), solar energy price (SEP), and carbon tax (CT). The results indicated that EAF-SES enjoyed lower LCCF but higher PC than EAF-TES. The LCCF of EAF-SES was 220 kg lower than that of EAF-TES. Besides, compared to the PC of 478 $ per ton of MS in EAF-TES, the PC in EAF-SES increased by 9.13%. While the electricity price was lower than 0.041 $/kWh or CT higher than 217 $/t CO 2 , respectively, EAF-SES had both significant advantages of economic and environmental benefits. Vigorously employing clean energy power generation, cutting down hot metal supply, and adopting full scrap melting mode in EAF steelmaking processes achieve better environmental performance. This research is expected to supply a technical idea for energy conservation, emission reduction, and cleaner production in future iron and steel industry. [Display omitted] • EAF steelmaking process integrated with solar energy system (EAF-SES) was developed. • Comparative life cycle assessment and techno-economic analysis of EAF-SES was conducted. • Life cycle inventories of 1536 heats in EAF steelmaking process in China were converged. • Some methods of energy conservation and emission reduction in EAF steelmaking process were brought out. [ABSTRACT FROM AUTHOR]

This paper presents an energy-sharing market coupled with carbon emission trading to quantify peer-to-peer energy-sharing markets' economic and environmental influences. We establish a bi-level peer-to-peer energy sharing market considering the interaction between energy prosumers and the system operator. An interaction algorithm and generalized Nash game are thus applied in this work. With the simulation results, we find that the daily disutility of provincial systems is reduced by about 7 ¥ when time-of-use electricity rates are applied. This pricing strategy can encourage prosumers to participate in energy-sharing transactions. The subsidies of electricity rates, carbon prices, and renewable energy investments can improve the social welfare of the whole system. Considering the development of power generation technology and emission reduction targets, the change of optimization parameters in the next 30 years is analyzed in the example. It can be concluded that the total CO2 emission in 2050 will be reduced to almost half of that in 2020 in the energy-sharing markets. The subsidy of renewable energy generation and stricter CO2 emission constraints may accelerate the reduction of carbon emissions. [Display omitted] • It is the first time to assess the environmental effect of peer-to-peer energy trading with actual data. • We analyze the peer-to-peer energy trading impacts on the carbon emission amount in China. • We compare the market participants' revenue and CO2 emission considering the different scenarios. • We develop a bi-level coupled emission right trading market to verify whether total emission is reduced. • The development of technologies and various energy structures are considered quantitatively. [ABSTRACT FROM AUTHOR]

Strict climate goals require a rapid reduction of carbon emissions across society. It is a considerable challenge to design decarbonization pathways considering co-operation in energy and carbon emissions across multiple sectors simultaneously. In order to obtain the decarbonization pathway for the whole society, this paper establishes a long-term multi-sector low-carbon transition pathway planning model. Besides inter-sectoral energy supply and demand, this study fully considers the allocation of carbon emissions quotas among different sectors by modified-grandfathering method, which takes into account both equity and efficiency principles. The decarbonization targets of different sectors, corresponding mitigation pathways and energy consumption can be calculated. China is taken as a case study. Results show that China's total energy consumption peaks at 5 billion tces around 2030, then falls below 4 billion in 2060, with the proportion of primary electricity and other clean energy reaching over 50 percent. Besides, the realisation of carbon neutralisation in China depends heavily on the improvement of carbon capture and storage (CCS) and end-use electrification rate. Moreover, the study of different quota allocation ways shows that the increase in the contribution rate of power system mitigation leads to the reduction of the total low-carbon transition costs, which also aggravates administrative difficulties. • A multi-sector model is set up to plan the long-term low-carbon transition pathway. • Modified-grandfathering allocation method can consider multiple indicator factors. • The clean energy consumption of China accounts for about 55% in 2060. • The end-use electrification rate in China is close to 70% in 2060. • The transition pathway with lower cost increases administrative difficulties. [ABSTRACT FROM AUTHOR]

Balancing environmental protection and economic development is critical in climate policy-making. The carbon trading policy is an important market-based climate strategy that aids in achieving the national carbon neutrality goals. However, empirical evidence at the industry level has not yet demonstrated whether there is an employment dividend. In this study, we constructed a theoretical framework to analyze the employment effects of carbon trading policies. We quantitatively evaluated the employment effects of policies in the pilot region using the differences-in-differences model. And we considered and explained the adjustment paths of employment dividends from the industry-level onwards. We found a "two-eight split" phenomenon in the employment effects of the carbon trading policy in China—the overall employment level of the region increases while employment in some regulated industries is inspired and it is hindered in others, shifting to other regulated industries and non-regulated regions. The empirical results revealed the following points. (1) After controlling economic and demographic factors, carbon trading policies can bring significant employment dividends to pilot areas. (2) While implementing such policies can improve the employment level of the paper industry, they can also seriously impact the employment development of the petrochemical and building materials industries. (3) Both energy consumption and technology levels effectively dampen the policy's negative employment effects and promote employment in the pilot areas. (4) The high carbon price levels within the range of carbon price fluctuation have inhibited the employment levels of the pilot industries. However, it still promoted the overall employment levels of the pilot areas through the employment transfer effect. As future carbon prices increase, the policy's regional employment dividend may decrease or disappear. Our findings illustrate the employment effect of industry-level carbon trading policies and provide a reference for improving carbon market construction. [ABSTRACT FROM AUTHOR]

Abstract There is much discussion about whether the rapid expansion of onshore wind power in China is sustainable, given the decrease in feed-in tariffs (FIT). It is unclear whether the recently launched nationwide carbon pricing system, which will include 1700 power companies, can compensate for decreasing FITs and possibly provide new incentives for wind power developments. This paper investigates the ability of carbon pricing policies to compensate for declining FITs in support of onshore wind power investment in China. First, we constructed a dataset of 2059 onshore wind power projects from China's thirty provinces between 2006 and 2015 to estimate the levelized costs of electricity (LCOE). This dataset was used to assess the profitability of each wind project for different carbon prices, varying levels of FITs, curtailment rate, and discount rate. Our findings suggest that the carbon price can compensate partially for the revenue loss caused by declining FITs as well as improving the profitability of projects. However, current carbon prices in China's carbon emission trading pilots are not sufficiently high to compensate for the revenue losses, especially under the grid parity scenario. Consequently, without FITs, the sustainable development of onshore wind power in China is uncertain. A sensitivity analysis of the effect of the carbon pricing policy demonstrates that in the case of higher investment risk and more serious curtailment, the effect of carbon pricing policy on promoting the wind power investment seems to be more significant. Highlights • A dataset of 2059 onshore wind power projects in China is constructed. • The LCOEs are estimated and the profitability is assessed with carbon pricing. • The carbon pricing policy can improve the profitability of the wind power projects. • The effect of current carbon price is limited under the grid parity scenario. • The effect becomes more significant in case of higher risk and curtailment rate. [ABSTRACT FROM AUTHOR]

This paper reviews the incentives for pursuing a low-carbon electricity sector that are embedded in China's regulatory and policy framework. To do so, we first describe the industry structure and the regulatory framework. Second, we explicitly review the policies that were developed to promote energy efficiency and renewable energy. These policies range from the introduction of legal requirements to undertake particular actions to pricing mechanism and financial incentives. Based on this, we tease out the challenges faced by a sector governed by a myriad of complex arrangements, different institutions and agents who face different and often conflicting incentives for pursuing environmental and energy efficiency objectives. Finally, we provide suggestions to scientifically set up low-carbon policies and to achieve the expected goals with minimum social cost. [ABSTRACT FROM AUTHOR]

As China's economy is transforming into a new normal stage, the energy industry is becoming increasingly innovation-driven, which is shown particularly in renewable energy. Given that the green innovation transformation is crucially critical during China's new normal stage, this paper analyzes the direct impact of energy consumption on energy innovation and innovation transformation, and reveals the net effect of green innovation transformation on economic sustainability and energy consumption. Micro and macro empirical evidence are both provided in this paper by implementing two estimation methodologies: fixed effect model and Generalized Method of Moments (GMM) Vector AutoRegression (VAR) approach, respectively. Estimating Chinese micro 153 listed energy companies' data and macro 30 provincial data from 2009 to 2016, we find that: (1) In terms of energy enterprises' innovation activity behavior, energy consumption could promote total energy innovation, of which renewable energy innovation is stimulated far more significantly. (2) There is an increasing tendency for energy structure to transform into renewables as the energy consumption increases. (3) The green innovation transformation, instead of total innovation counts, can reduce energy consumption and benefit economic sustainability. Therefore, the structure of innovation transformation plays more significant role than total energy innovation in stimulating economic growth, and alleviating the energy consumption antinomy during China's new normal stage. In this regard, in order to stimulate green energy transformation, government should implement financing policy, reduce administrative intervention, upgrade energy legal system and improve energy pricing mechanism to encourage more research and development (R&D) activities at the core of renewable energy. • Provide evidence from both micro- and macro-economic perspectives. • Energy consumption stimulates renewable energy innovations more. • Transformation towards renewables is driven by high energy consumption. • Green innovation transformation decreases energy consumption in the long run. • Green innovation transformation benefits economic sustainability. [ABSTRACT FROM AUTHOR]

Emission trading scheme (ETS) is considered as a tool to reduce emissions at a low cost, which can effectively promote the carbon peak and carbon neutrality. As the main participants in the carbon emission trading (CET) market, the behavior and strategies for governments and different types of enterprises affect the final emission reduction performance. Taking the national carbon market as an example, this paper constructs a tripartite evolutionary game model including governments and power generation enterprises with different emission reduction capacity to analyze the possible game situations in the development process of the CET market. The results show that with the development of the CET market, the governments and power generation enterprises will have different game results. In order to achieve the optimal stable equilibrium and maximize carbon emission reduction, governments should improve regulatory efficiency and formulate appropriate incentive strategies for excess emission reduction and punishment strategies for insufficient emission reduction. In addition, the initial willingness will not affect the final strategy choice of the three parties. Reasonable free carbon quota ratio, lower technology emission reduction cost and higher carbon price can stimulate the enthusiasm of governments and enterprises to reduce emissions. The above results provide a reference for coordinating the relationships between governments and power generation enterprises with different emission reduction capacity in the construction of the CET market in the future. [ABSTRACT FROM AUTHOR]

The coverage of Emission Trading Scheme (ETS) has been widely discussed. Based on the plans of the various stages of the National Development and Reform Commission in China, we explore the different impacts of different plans by applying recursive dynamic computable general equilibrium. What we found is different. Covering more industries, carbon prices will be lower so that the emission reduction motivation of each covering enterprises will be reduced. However, the resource attributes of carbon emission allowance will gradually emerge. In general, we found that the changes in the sectoral coverage will bring about the changes in the two effects in ETS market: carbon price effect and the allocation effect. The interaction of the two effects has led to a positive impact of both low-coverage and high-coverage on reducing emissions and improving the energy efficiency. This paper suggests that just covering three enterprises that are easy-to-MRV (monitoring, reporting, and verification) is enough for emissions reduction: cement, chemical and electric power, which can also achieve high emission reduction efficiency and can effectively reduce supervision cost. • A dynamic recursive CGE model is established in this paper. • There are two effects in ETS market: carbon price effect and the allocation effect. • Reduction efficiency is an inverted U-shaped curve to the changes of coverage. • Cement, chemical and electricity are encouraged to participate in ETS. [ABSTRACT FROM AUTHOR]

Motivated by reducing carbon emissions, carbon trading market have been opened to promote environmental protection. Accurate carbon trading volume and price forecasts have far-reaching implications for environmental and energy policy formulation. As the country with the most massive carbon emissions in the world, China's carbon price trends, carbon trading volume trends, and policy orientation are of great significance. As matters stand, there are few studies on carbon price and trading volume forecasts, and no research paper has covered carbon price and trading volume forecasts for all carbon trading markets in China. In order to provide a relatively complete reference for the carbon price and trading volume in China, this paper uses six machine learning models to predict the daily carbon price and trading volume of eight carbon markets in China, including Beijing, Shenzhen, Guangdong, Hubei, Shanghai, Fujian, Tianjin, Chongqing. The prediction models include extreme gradient boosting, random forest, kernel-based nonlinear extension of the Arps decline model optimized by grey wolf optimizer (GWO-KNEA), support vector machine optimized by particle swarm optimizer, support vector machine optimized by fruit fly optimizer and simulated annealing algorithm, and radial basis function neural network (RBFNN). Moreover, an advanced data denoising method, i.e., complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), is used in the models to smooth the raw data. Because the eight carbon trading markets have different carbon price and trading volume distribution characteristics in time series, by comparing the prediction results, more suitable models for the specific carbon market can be obtained. The prediction results show that CEEMDAN-GWO-KNEA and CEEMDAN-RBFNN are more competitive in predicting carbon prices and trading volume in China because of their best performance in many datasets. The average accuracy of CEEMDAN-RBFNN and CEMDAN-GWO-KNEA in carbon price prediction is 98.40% and 97.89%, respectively. The predictive stability of each model in different application scenarios is also discussed. The results show that high prediction accuracy does not mean better prediction stability, but CEEMDAN-RBFNN and CEMDAN-GWO-KNEA can still guarantee high stability in most datasets. [ABSTRACT FROM AUTHOR]

Emissions trading system (ETS) is an effective approach to achieve China's "double carbon" targets. An accurate measurement of carbon MAC (marginal abatement cost) serves as the basic evidence for the stability of ETS and market validity. This paper constructs a comprehensive analytical framework, including four parametric and two non-parametric methods, to analyze the efficiency and carbon MAC of 92 large coal-fired power plants in China. Results show that the average technical efficiency and shadow price were 0.92 and 13.63 RMB/ton, respectively. As verified by Monte Carlo simulations, each method has its cons and pros, while the MAC calculated by the parametric stochastic frontier analysis (SFA) is the closest to the real carbon market price. Additional analyses show that the MAC is positively related with plant scale and negatively related to the provincial carbon intensity targets. This result suggests power plants' technological progress and the asymmetric government subsidies. [ABSTRACT FROM AUTHOR]

Sectoral coverage is a chief challenge in the design of carbon mitigation policies, this paper addresses such a concern within a hybrid regulation setting in which a carbon tax is adopted for complementary regulation of emission sources outside the Emission Trading System (ETS) in China. With a focus on the efficiency of the hybrid system, the regulated sectors are identified by linkage analysis and the resulting price changes is captured by the input-output (I-O) price model. The simulation shows that, in terms of the price incentives, the proposed carbon tax is a necessary complement to the ETS, in respect of both magnitude and structure. As a result, a mixed policy setting can be expected to have an efficiency advantage over the ETS alone. Understanding of the joint determination of the sectoral coverage and the corresponding price incentive effect is a new contribution to the theoretical literature on designing market-based carbon policies, which has substantial policy implication for the future design of instruments targeted at achieving China's post-2020 mitigation commitments. [ABSTRACT FROM AUTHOR]

This paper focuses on the estimation of carbon shadow price (CSP) by applying the by-production (BP) model within a nonparametric framework. The extended BP model ensures the meaningful shadow price estimation and linkages between the sub-technologies that define production and pollution activities. We focus on the instance of the Fare-Grosskopf-Lovell index that is important for empirical analysis due to its non-radial nature. The model is applied to China's industrial sector, which has undergone considerable development in recent decades. Data from 1998 to 2017 are used for the analysis, and the results suggest that the eastern region performs the best. In general, the CSP has been increasing in all regions, suggesting that cleaner energy has been introduced into the energy mix. Further promotion of the renewables would result in a decline in the CSP. The comparative analysis is carried out to check the effects of the axioms on the resulting estimates of the CSP. [ABSTRACT FROM AUTHOR]

Carbon emission right allowance is a double-edged sword, one edge is to reduce emission as its original design intention, another edge has in practice slain many less developed coal-consuming enterprises, especially for those in thermal power industry. Partially governed on the hilt in hands of the authority, body of this sword is the prices of carbon emission right. How should the thermal power plants dance on the blade motivates this research. Considering the impact of price fluctuations of carbon emission right allowance, we investigate the operation of Chinese thermal power plant by modeling the decision-making with optimal stopping problem, which is established on the stochastic environment with carbon emission allowance price process simulated by geometric Brownian motion. Under the overall goal of maximizing the ultimate profitability, the optimal stopping indicates the timing of suspend or halt of production, hence the optimal stopping boundary curve implies the edge of life and death with regard to this enterprise. Applying this methodology, real cases of failure and survival of several Chinese representative thermal power plants were analyzed to explore the industry ecotope, which leads to the findings that: 1) The survival environment of existed thermal power plants becomes severer when facing more pressure from the newborn carbon-finance market. 2) Boundaries of survival environment is mainly drawn by the technical improvements for rising the utilization rate of carbon emission. Based on the same optimal stopping model, outlook of this industry is drawn with a demarcation surface defining the vivosphere of thermal power plants with different levels of profitability. This finding provides benchmarks for those enterprises struggling for survival and policy makers scheming better supervision and necessary intervene, and the methodology developed in this paper provides a research paradigm for enterprise survival analysis. [ABSTRACT FROM AUTHOR]

The switch of coal to gas in power generation enterprises is of great significance for tackling air pollution and mitigating climate change. Under China's emissions trading system, power generation enterprises have incentives to switch to low-carbon clean power, and the cost-benefit of switching is the basis of their decision-making. To this end, this paper studied the impact of changes in carbon and fuel prices on switching prices. The results show that: (1) For per yuan increase in the amount of natural gas, coal, and carbon, the switching price increased 4.768, decreased 5.845 and decreased 0.9994 yuan, respectively. (2) The carbon price is relatively low, and the impact of it on switching price is small. (3) The fluctuation in the carbon price, coal price and the natural gas price has a hysteresis effect on switching price. [Display omitted] • This study assesses the coal-to-gas switch possibility in Guangdong Province. • A new mode of operation of coal-to-gas switch is published. • For per yuan increase of carbon price, the switching price decreased 0.99 yuan. • The impact of carbon price fluctuations on switching price has a lag effect. [ABSTRACT FROM AUTHOR]

Water Saving Management Contract (WSMC) is believed as an efficient water-saving model and developed in recent years. However, because the high upfront investment cost and great uncertainty of WSMC, the static evaluation methods have defects in evaluating the return on investment of WSMC projects. Real option theory makes up for these defects and is applied in this paper with the expectation to evaluate the uncertainty of a Shared WSMC project and the economic feasibility of investing in the project. First, a new expression of volatility is presented based on the characteristics of Shared WSMC projects. The volatility contains uncertainties in water price, the amount of water-saving, waterborne fee, and operating & maintenance cost simultaneously. Then, it is assumed that the water saving service company is entitled options to abandon and expand the Shared WSMC project during the contract period. Based on the existing model, the exercise prices of the two options are improved. Furthermore, the real option value (ROV) of a Shared WSMC project is solved using the binomial tree option pricing model, and the economic feasibility of the project is comprehensively evaluated by combining with the static net present value and ROV organically. Finally, the Shared WSMC project of the Hebei University of Engineering in China is studied to verify the validity of the proposed method. The obtained results indicate that the economic value of the Shared WSMC project is expanded by the ROV, which means that additional value can be gained through flexible strategies and management in the context of uncertainty. Besides, the additional economic benefits of the two options vary significantly for different Shared WSMC projects. Expand option is particularly essential when Shared WSMC projects with a considerable return on investment. Conversely, the abandon option should be taken seriously. Especially when both options are considered together, the additional economic benefits are sub-additive. • Based on Monte Carlo simulation, the volatility of project value is measured synthetically by several key factors. • The time value of the fund is emphasized according to different discount rates, and then the exercise price is determined. • The model of option pricing with modified volatility and exercise price is applied to the Shared WSMC project firstly. [ABSTRACT FROM AUTHOR]

To investigate the impacts of regulatory announcements on China's pilot carbon markets, as well as test the semi-strong form of market efficiency, a multivariate regression model with GARCH effects combined with multiple bilaterally modified dummy variables is established which allows us to investigate the potential overlapping influence from two timely adjacent events. The Shanghai, Guangdong and Hubei pilot carbon markets are taken as case studies. Based on the ADF test and the runs test, the results show that the China's pilot carbon markets have achieved the weak form of efficiency but not the semi-strong form of efficiency. With the regression model the results show that the release of the allowance allocation plan will affect the carbon price trend, and the allowance auction can only play an auxiliary role as its impact on the carbon price is not decisive. In addition, ubiquitous ex-ante impact proves that the information leakage exists in each pilot carbon market. The model established in this paper is suitable for analyzing the impacts of overlapping events in different markets, which can effectively assist decision makers to distinguish the effects caused by overlapping events. [ABSTRACT FROM AUTHOR]

Cargo transportation is one of the major sources of carbon emissions. To reduce carbon emissions from inland freight, one way is to encourage rail transportation instead of truck transportation. In this paper, a new approach combining railway operation management, revenue management, and policy formulation is proposed to increase the market share of railway freight and reduce carbon emissions. Through the integrated optimization of operation planning and pricing, the competitiveness of railway freight services is improved. The carbon tax policy acting on the demand-side can affect consigners' choice behavior. The low-carbon subsidy policy acting on the supply-side can encourage rail transport. Then a bi-level multi-objective model is proposed to achieve the trade-off among the three stakeholders (the government, rail service operators, and consigners). The complexity of the bi-level multi-objective problem motivates the development of a hybrid optimization algorithm that combines the Non-dominated sorting genetic algorithm III (NSGA-III) and the Descent algorithm based on sensitivity analysis (SAB). Through a real case based on a rail service network with nine nodes in China, the rationality of the proposed methodology is verified. The results show that the approach proposed can achieve a 39.27% modal shift (MS) from road to rail, as well as a 37.09% carbon emission reduction. [ABSTRACT FROM AUTHOR]

China is the world's largest developing country and a carbon dioxide emitter. A functional carbon market can effectively reduce carbon emissions. This paper uses the Markov chain Monte Carlo-stochastic volatility model and the wavelet multi-resolution model to analyze the volatility of price returns and the dynamic characteristics of price fluctuations in the carbon pilot markets in Hubei, Shanghai, and Shenzhen. The price movements in these markets are compared to the emissions trading system of the European Union (EU-ETS). The results show that there is a volatility clustering in the price of carbon trading in Hubei, Shanghai, Shenzhen and the EU-ETS. China's carbon pilot markets have a deficiency in terms of volatility stability, as does the EU-ETS. From a long-term perspective, China's carbon market lacks a detailed development plan, which is vital because the construction of the market system is not yet optimal. From a short-term view, China's carbon market is not active and the participants' attitude toward risk is extremely sensitive. • MCMC-SV and wavelet multi-resolution model applied to carbon price volatility. • Three major Chinese markets are compared to the EU-ETS. • All markets evidence volatility clustering. • China's carbon market lacks a detailed development plan for the long term. • Short-term, China's carbon market must become more active and offer more products. [ABSTRACT FROM AUTHOR]

This paper aims to provide a comprehensive look at the potential price effects of a carbon tax at the sectoral level in Chinese economy. From a production perspective, the sectors subjected to the levy are discussed based on sectoral linkage characteristics, and then the tax-induced repercussions of output prices are simulated using an extended I–O price model. The results, based on the Chinese economy for the year 2012, show that levying a carbon tax on sectors with above-average CO 2 emission linkages and non-significant production linkages will increase their output prices by an average of 1.93%, while induce relatively slight price increases in the encouraged group (0.30%) and the other group (0.34%); the household is the least affected sector (0.28%) in the economy. This means that an appropriate tax design tends to shift intermediate inputs and expenditure toward less CO 2 -intensive goods and services by its uneven cost-push impacts on production sectors, and the entire economy will ultimately get affected. By establishing techniques for policy evaluation, our analysis highlights the value of linkage analysis in the design of a carbon tax and brings more clarity to price transmission after the imposition of a carbon tax in the economy; consequently, the indirect effect of carbon tax on household can be better understood. Image 1 • Special attention should be given to the price transmission caused by a carbon tax. • Sectors subjected to the levy should be identified based on linkage characteristics. • The tax-induced price changes are captured by an extended I-O price model. • The proposed tax design tends to shift the production structure toward low-carbon. • The proposed method is a useful tool for value options aimed at pricing carbon. [ABSTRACT FROM AUTHOR]

China's carbon market has become increasingly active in recent years, and thus it is of great significance to study the characteristics of the carbon allowance price fluctuation for the healthy and steady development of this market. As leverage is common in asset prices in general financial markets, this paper adopts the leverage stochastic volatility (SV-L) model to characterize the price volatility of the five pilot carbon markets in China. We first make a Bayesian inference for the SV model, then construct a Monte Carlo calculation process based on Gibbs sampling for empirical analysis, and finally compare the SV-L model with the normal stochastic volatility (SV–N) model. The results show that the carbon price fluctuations of the five pilot markets are quite different. Among them, Shenzhen, Guangdong, Shanghai, and Beijing have a "positive leverage effect", and Hubei has an "anti-leverage effect". Through comparative analysis, we find that the SV-L model is superior to the SV-N model in terms of the degree of data fitting and simulation ability. Finally, we offer suggestions on the development of China's unified carbon market. • We explore the characteristics of carbon price fluctuation in different pilot markets in China. • The SV-L model indicates that the carbon price fluctuation of each pilot province or city shows great difference. • Five pilot carbon markets show different degree of leverage effect. • The SV-L model is superior to the SV-N model for characterizing the carbon price of pilot markets in China. [ABSTRACT FROM AUTHOR]

Carbon price forecasting plays a vital role in establishing a reasonable and stable carbon market. A number of carbon price forecasting models have been developed to improve the effectiveness of the predictions. However, most of the previous studies failed to focus on the role of choosing the appropriate input features and only aimed to improve the forecasting accuracy. In this paper, a novel hybrid model based on feature selection and a multi-objective optimization algorithm is proposed for carbon price forecasting. More specifically, the main novel contributions of this study are as follows. A two-stage feature selection method is developed to obtain the appropriate input variables to enhance the forecasting ability. In addition, the weighted regularized extreme learning machine is optimized using a multi-objective optimization algorithm, named the multi-objective grasshopper optimization algorithm, which can obtain better forecasting results. To demonstrate the effectiveness of the developed carbon price forecasting model, two daily carbon price datasets that were collected from the China and European Union Emissions Trading Scheme, are used in this study. The results revealed that the mean absolute percentage errors of the proposed model utilizing data from the China and European Union Emissions Trading Scheme are 2.4923% and 0.8418%, respectively, which are lower than those of other compared models. In addition, the variances of the forecasting errors of the developed model are 1.1419 and 0.0038 for the data from the China and European Union Emissions Trading Scheme, respectively. These results reflect the superior forecasting ability of this method compared to other methods. Therefore, the proposed method is more effective than other models in carbon price forecasting. • A novel hybrid forecasting architecture is proposed for carbon price forecasting. • A two-stage feature selection method is proposed to gain optimal input variables. • A multi-objective optimization algorithm is used to improve the prediction results. • The developed model can perform better using both EU ETS and China data. [ABSTRACT FROM AUTHOR]

Through a series of policies release, the trading system of China's carbon market is being improved, but its current liquidity is still low. To improve market liquidity, this paper analyzes the relation between the release of policy related to carbon market operations and the volatility of carbon price, thus arranging an optimal adjustment scheme for government's policy-making. Through the jump test and Logit regression, we investigate historical policies and prices of 7 Emission Trading Scheme pilots in China to explore the impact of policy release on carbon price, and then quantify the effectiveness of these policies. Empirical results indicate that in the short-term, new policies can rapidly lead to a large volatility of carbon price, but it can also reduce the abnormal volatility with the spread of price information. Further, the demand-stimulus policies promote a positive volatility of carbon price. In the long-term, the insufficient demands for carbon emissions quota raise the disappearance of market dividends caused by the policy release and lead to an excessive call-back of carbon price, thus increasing mid-and long-term risks in carbon markets. Additionally, China's macro environment policy can stabilize carbon price, and thus an effective tool for improving the liquidity of carbon markets is developing demand-stimulus policies. • We analyze the origin of liquidity in China's carbon market from the perspective of policy release. • We obtain the price effect of carbon policy under different policy categories. • We judge the influence cycle of different policies on carbon price based on price jumps after policy release. • We propose a policy tool to improve the liquidity of China's carbon market based on the feedback of policy to carbon price. [ABSTRACT FROM AUTHOR]