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This paper presents a comprehensive content analysis-based literature review of studies on Inventory Routing Problem incorporating sustainability-related aspects. A total of 40 journal articles are selected from Web of Science and Google Scholar databases, and are reviewed by categorizing them into single-objective and multiple-objective models. The review suggests that studies with single-objective models covered areas including waste management (in reverse logistics models), returnable transport item management (in closed-loop supply chain models), waste prevention and reduction (in models with perishable products), and emission reduction (in models with emission caps and carbon price). Studies on reverse logistics are limited to collecting and storing waste at a collection site, while including waste processing and disposal operations could make them more applicable. Studies on closed-loop supply chains are few with a focus on managing returnable transportation items. Future studies could consider remanufacturing, recycling and disposal of products for efficient waste management. Most of the papers in logistics of perishable products considered waste prevention rather than waste reduction. Previous studies related to emission reduction using carbon policies mainly focused on emissions from transportation. Including emissions from other logistics operations could be useful from a practical perspective. Relatively few studies developed multi-objective models. Environmental objectives in multi-objective models focused on minimizing total emissions. Few studies considered social objectives related to minimizing inventory of hazardous material and infectious medical waste, and improving customer satisfaction. Other social aspects such as those related to traffic congestion and job creation could be considered in future studies. Furthermore, application of efficient models and solution approaches to real case studies could be valuable.

There are three main emission reduction modes: manufacturer-only emission reduction (M-ER), retailer-only emission reduction (R-ER), and joint emission reduction (J-ER). This paper develops an analytical framework consisting of a manufacturer and a retailer to explore the optimal choice of emission reduction mode under the carbon cap-and-trade mechanism. The paper firstly obtains the optimal decision for each mode using Stackelberg game model and conducts a comparative analysis. Then, the paper examines the effect of the three modes on the environment, consumer surplus, and social welfare. Finally, this work further studies the impact of low-carbon effect and carbon price on the equilibrium results of the three modes. The findings show that the level of emission reduction is the highest in the J-ER mode. Firms prefer the single emission reduction mode if their abatement cost coefficient is small but the J-ER mode if the coefficient is large. Moreover, the highest consumer surplus and social welfare are generated in the J-ER mode and the carbon price can effectively adjust a firm's emission-reduction level. In addition, the stronger the low-carbon effect, the more obvious the difference in emission reduction effect under the three modes. This study contributes to the growing body of research on low-carbon supply chain to provide insights on firms' emission reduction strategies.

The future of the Carbon Market Approach under the United Nations Framework Convention on Climate Change (UNFCCC) is uncertain. However, further development of Carbon Markets is expected. This paper estimates the average carbon price of the carbon emission permits (COP-approved units) acquired by the Spanish government and also a more realistic figure for the average carbon cost of the aforesaid emission permits. This is very useful for evaluating and comparing different carbon prices and markets. In addition, the paper contributes to the existing literature by providing a deeper understanding of the flow of emission permits between the European Union Emission Trading Scheme (EU ETS) and the Emission Trading Scheme (ETS) under the Kyoto Protocol. The main conclusions are that the estimated carbon price and cost are both lower than other reported prices and costs. This may go some way towards explaining why the government preferred to use the Flexibility Mechanisms and acquire an especially large number of COP-approved units (mainly Assigned Amount Units (AAUs)) through bilateral transactions with other Annex B countries, instead of launching actual greenhouse gas (GHG) abatement projects in the country. Moreover, more transparency is needed in regard to bilateral transactions of AAUs between the Spanish government and other Annex B seller countries.

This paper examines manufacturer manufacturing/remanufacturing planning issues in which a downward substitution strategy is applied between new and remanufactured products and presents three mathematical models to determine the optimal production quantities of a new and remanufactured product that maximize the total profits when considering capital and/or carbon emission constraints. These models include sales revenues, manufacturing/remanufacturing costs and revenue from or cost of carbon credit trading. Finally, four numerical examples are provided to explore the comparison results for the optimal manufacturing/remanufacturing decisions, total profits and carbon emissions as well as to analyze the impact of parameters under different constraints, such as the initial capital, carbon cap and carbon price on the optimal production policies. The results indicate that the capital constraint can encourage the manufacturer to remanufacture used products at a higher quality level and significantly reduce carbon emissions. However, the carbon emission constraint can always encourage the capital-constrained manufacturer to produce more remanufactured products to achieve maximum profit. In addition, it is found that the manufacturer need more capital to achieve the maximum profit when the carbon emission constraint is considered. Furthermore, when the capital constraint is considered, the carbon emission constraint will have more distinct influences on the production policies of the manufacturer. Finally, the results proposed in this paper provide applicable suggestions to manufacturers and policy-makers.

Carbon capture and storage (CCS) is an attractive option to help reduce China's carbon emission. This paper analyzes the impacts of subsidy for electricity generation on CCS retrofitting investment and carbon abatement in China's coal power plants, considering the coaction of carbon market and subsidy policy. To this end, this paper builds up a real option model, considering carbon prices, electricity prices and coal prices' uncertainties. For decision of CCS investment, we consider two important aspects: whether to invest and when to invest, i.e. CCS investment's potential and timing. Some major findings include: 1) The impacts of subsidy on CCS investment and carbon abatement are affected by carbon market conditions: initial carbon price can determine the magnitude of subsidy's impacts, while carbon prices' uncertainty level can determine subsidy's marginal impacts. 2) Subsidy from $0.01 to $0.05/kWh can raise CCS investment potential by 9.66%–39.18%, shorten CCS investment period by 0.39–1.95 years and bring carbon abatement potential of 0.10–1.89 GtCO 2 , considering different carbon market conditions. 3) With the benchmark carbon market and affordable subsidies, triggered CCS investments in coal power plants have an economical-efficient carbon abatement potential up to 20% of China's carbon emission in 2030.

Curbing carbon emissions has been a hot topic of public concern around the world. Although the Cap-and-Trade (C&T) system has excellent environmental performance, its effectiveness in achieving low-carbon emissions objectives is not enough. Practices show that carbon emission reduction technology (CERT) investment by micro-manufacturers can make up this limitation. In the context of C&T system, this paper develops a dual-objective non-linear programing model of CERT investment strategies based on matching game between the oligopolistic manufacturers (oligo-manufacturers) and suppliers. Maximum production capacity (MPC) of oligo-manufacturer is considered. A matching game mechanism of bilateral stakeholders is proposed with consideration of vertical and horizontal aspects. From the vertical aspect, there is a matching analysis between a finite number of oligo-manufacturers (offering investment funds) on one side and a finite number of suppliers (providing CERTs) on the other side. From the horizontal aspect, there is Cournot game among the oligo-manufacturers. The model proposed in this paper enriches the literature on the selection process of CERT investment strategies. The impacts of oligo-manufacturer amount, green investment coefficient and carbon price on the investment-production decision-makings are finally analyzed by numerical methods. The results show that stable matching outcomes (i.e., the optimal joint production quantity, CERT investment intensity, emission reduction and emission trading quantity) can be obtained by constructing the Lagrange function and solving Karush-Kuhn-Tucker (KKT) conditions. This means that the matching game mechanism helps optimize the CERT investment and production mode. In addition, intense competition among oligo-manufacturers is conducive to reducing carbon emissions. A higher carbon price leads to a higher economic profit, and a higher green investment coefficient leads to a lower carbon emission objective. MPC acts as a drag on development of the oligo-manufacturer, which can be used to improve the profit structure of stakeholders within a certain scope.

The paper presents a cleaner production and distribution model for global facility network that offers reverse logistics services under the warranty period. Hybrid manufacturing and storage facilities with flexible capacity are considered to save the extra setup and maintenance cost linked with establishing new reverse logistics facilities. The carbon emissions are accounted for the various activities related to product manufacturing and return. The carbon cap and trade policy is considered for regulating emissions. The dynamic mixed-integer non-linear programming (MINLP) model discussed in the paper optimizes the total cost of the network including costs associated with reverse logistics. The model is analysed on two hypothetical data sets. The results for the smaller data set reveal that the inventory holding is a better alternative to capacity expansion to deal with fluctuations in market demand. However, for the bigger data set, capacity expansion is recommended to satisfy the increased demand instead of relying completely on inventory management. The results are further analysed for varying carbon price and it is observed that increase in carbon price initially causes an increase in the carbon emissions but later it starts decreasing. This indicates that the increase in carbon price does not provide quick results and is more effective in controlling the emissions in the longer run. Furthermore, the proposed model investigates how different trade policies viz. free trade agreement (FTA), FTA with cap, and an increase in tariff barriers influence the total carbon emissions and the total cost. The results indicate that the total cost and the total emissions are relatively less under FTA with cap, however, there is a slight increase in the emissions of one country.

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.

The energy used to move freight comprised about 17% of all the energy consumed in the United States in 2010. This percentage was an increase from 8.7% in 1970 even though fuel efficiency gradually improved over the period. One major reason of this phenomenon is that the longer supply chains of many industries caused by globalization increase freight movement. Various carbon policies have been discussed and even implemented for some industry sectors in the European Union to reduce carbon emissions. However, the emissions of surface transportation have not been well studied. This paper investigates three of the most common carbon policies: carbon emission tax, inflexible cap, and cap-and-trade. The impact on a company may include the redesign of its supply chains and different choices of transportation modes (truck, rail, or waterway). This paper proposes optimization models for major retailers, who make a huge contribution to freight movement, to design their supply chains under various carbon policies. The results from the models can help policy makers to predict the impact of policies on overall emissions in the freight transportation sector. The model may also be incorporated into the integrated assessment models for climate change analyses. Furthermore, a sensitivity analysis is conducted to study the impact of the policy parameters on carbon emissions and logistics cost. This research uses the supply chain of a major U.S. retailer (i.e., Wal-Mart) to demonstrate how to use the models and conduct the sensitivity analysis. In order to realize significant emission reduction, a redesign of supply chains of major retailers is necessary and will be triggered only by a high carbon tax rate, a high carbon price, or a very strict carbon cap, which all may involve high social and/or economic complexity. Numerical experiments show that different policies have different impacts on the costs and the effectiveness of emission reduction. How to choose policy parameters is critical to the effectiveness of a carbon policy. The different sensitivity also implies different challenges during the implementation of the policies, such as the high cost preventing retailers from accepting any carbon taxes and the difficulty of reaching an agreement on the carbon caps between retailers and policy makers.

This paper aims to provide an improvement in the modeling of supply chain designs by incorporating correlated uncertainty among multiple parameters, resulting in a more resilient design. A new methodology to generate forecasts for historically correlated time series, regardless of their underlying probability distributions, is presented and applied to generate scenarios for energy and carbon prices, which historically proved to be correlated. These scenarios are then used in a stochastic computation to obtain a three-echelon supply chain design in Europe maximizing the economic performance. The emissions were monetarized through the incorporation of the European Union cap-and-trade emissions trading system into the model. The social impact of the supply chain network is measured in terms of the direct, indirect and induced jobs it creates, which are proportional to the economic performance. By combining the developed methodology with data mining algorithms, a reduction in the number of required scenarios by more than 90% was achieved. The numerical case study moreover shows that the stochastic design ensures an average reduction of emissions by more than 3 ktons compared to the use of a deterministic approach. In comparison, the computation of a stochastic supply chain design without parameter correlation takes 5 times longer. The authors gratefully acknowledge financial support to the Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital of the Generalitat Valenciana, Spain, under project PROMETEO/2020/064 and project PID2021-124139NB-C21.

Carbon dioxide emissions pricing is employed as an approach to mitigate climate change. To analyze the impact of carbon prices on the metal mining industry, this paper presents a new block-based carbon dioxide emissions estimation model to assess the carbon dioxide emissions and carbon intensity in varying carbon prices, by employing the engineering-based inputs from life cycle of mineral production. Such engineering-based data of an iron case is provided to test the impact of carbon prices on mining projects. The results indicate that the negative impact of carbon prices on the profit of mining projects is more sensitive in low minerals prices, especially for underground method. On the other hand, carbon price rising facilitates the mitigation of carbon dioxide emissions, by lowering the ratio of excavated waste rocks to mined-out raw ore, which varies linearly with the emissions. The carbon dioxide emissions in the studied case decrease from 39.92 kg carbon dioxide eq per ton to 21.50 kg carbon dioxide eq per ton, and from 51.02 kg carbon dioxide eq per ton to 42.80 kg carbon dioxide eq per ton, for surface and underground method, respectively. The results of carbon intensity show that it keeps increasing due to carbon price rising, until the mining boundaries are optimized based on the increased carbon price.

This paper investigates the information spillover between carbon price returns and 18 top European electricity companies' stock returns using a connectedness network approach. Our findings show that there is strong information interdependence between carbon price returns and electricity stock returns, as evidenced by a high total connectedness index. In our constructed carbon-electricity market system, the carbon market behaves as an information recipient, receiving varying degrees of information spillover from various power enterprises. Large electricity companies tend to contribute more information to the system than small ones. In addition, our dynamic results show a high, but also volatile, spillover index, which can be a result of changes to the carbon market situation and the financial markets' stability. Finally, an asymmetric effect can be identified when comparing the difference between negative and positive return connectedness networks.

The European Union Emissions Trading System (EU ETS) has strengthened the information flow and connection between carbon market and energy markets, which makes the carbon-energy system more complicated. This paper investigates information linkages and dynamic spillover effects between the carbon and energy markets. We adopt a systemic time-series approach to study connectedness in both returns and volatility in the carbon-energy system. Moreover, a rolling-windows method is used to show the dynamic features. Empirical results show that Brent oil prices play an important role in affecting carbon price changes and risks. Feedback exists from the carbon market to other energy markets, and electricity prices are shown to be the biggest information receiver in the system. It is also shown that the level of connectedness in the volatility system is substantially higher than that in the returns system. Our results can provide useful implications for policymakers to design market mechanisms and market investors to manage their portfolios.

More and more countries and regions have turned to the cap-and-trade scheme to control carbon emissions. Proper planning of emission permit trading and green technology implementation is beneficial for a generating company to achieve its emission targets at the lowest possible cost. In this paper, a multistage mixed-integer nonlinear programming model is formulated to help make these decisions, which minimizes the total cost of green technology investment, emission permit trading and holding. Differing from previous research, this model captures the characteristics associated with green technology, notably its implementation lasting as a project for several periods and investment cost declining over time due to technology maturation. The holding cost of emission permits as a new operational resource under the cap-and-trade scheme and auto-regression of the carbon price are also considered in the proposed model. The analytical results suggest the optimal trading period and the trading amount of emission permit as well as the starting period for implementing green technology. It is found that the optimal trading period has nothing to do with initial emission permit quota allocated by regulator. In addition, whether or not to invest in green technology is determined by balancing the investment cost and its resulting benefit of reduced emission. Higher carbon price and unit holding cost of emission permit will incent the generating companies implement green technology. The analysis results also illustrate the specific regions in which cleaner/dirtier green technology will be selected or none of them will be considered. Finally, numerical examples validate the analytical results.

This paper presents a real-options approach to assess the value of retrofitting carbon capture and storage technology to an existing natural gas-fired base-load power plant. Operating in a deregulated electricity market, a power plant owner seeks to decide whether to retrofit and at what techno-economic conditions to retrofit carbon capture and storage. The value of the plant is determined based on clean spark spread options. Two alternative carbon capture and storage technologies, post-combustion and oxy-fuel combustion, are evaluated. Price uncertainties of electricity and natural gas are modeled as mean-reverting processes. The plant is abided by the emission reduction policy of carbon tax or price. Results show that a plant owner would opt for retrofitting post-combustion technology to an existing power plant if the carbon price hits to at least 140 dollars per ton of carbon dioxide, and would select oxy-fuel combustion technology if the carbon price moves further to 185 dollars per ton of carbon dioxide or above. Since parameters of carbon capture and storage vary widely across the literature, sensitivity tests of the expected values to different costs, prices, and volatility parameters are also presented for an insightful comparative view of which carbon capture and storage technology to adopt at what techno-economic conditions.

The market of electric taxi has achieved rapid development in China recently, while further development is hindered by technical factors such as battery mileage and charging. First-tier cities as Beijing have explored and innovated in electric taxi business models to make up for the deficiencies caused by technological immaturity at the present stage. From the perspective of value chain, this paper carries out an analysis on current status of electric taxi business models in Beijing, and proposes a model for analyzing its economy. Moreover, a comparative analysis was conducted on the economy and sensitivity of battery changing and battery charging models based on field research data acquired from Pinggu District of Beijing. The research findings are as follows: a) overall, the battery changing model is better than the battery charging model economically. b) the sensitivity of the battery changing model to project period, carbon price and charging price is significantly higher than that of the charging model, because of its higher service frequency and greater service quantity. c) among all the influencing factors, charging price has the greatest influence on the two models. d) in the battery changing model, the breakeven point of the whole business model gradually declines as electricity price rises, so the battery changing model still can survive even without government subsidies. e) owing to vehicle loss, battery loss and other reasons, the two models have an optimal period respectively, but the greater loss of the battery changing model results in that its optimal project period is shorter than that of the battery charging model.

This paper presents multi-criteria environmental and economic analyses of pulverized coal power plants with various advanced CO2 capture and separation (CCS) technologies, including oxy-fuel combustion (Oxy), calcium looping post-combustion capture (CaL), combination of Oxy with CaL (Oxy-CaL) and Oxy with chemical looping air separation (Oxy-CLAS). The life cycle analysis (LCA) and techno-economic analysis (TEA) are integrated with Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approaches. This methodology is applied to forecast the potential of incorporating CaL or CLAS into oxy-fuel combustion and identify the most promising CCS technology option for pulverized coal power plants from the perspectives of different stakeholders. The results show that application of CCS reduces the ecosystem quality and the human health impacts, but increases the resources use and yields an economic penalty of $12.76∼$33.33 per ton of CO2 avoidance. From the perspective of industry only, CCS has an unfavorable effect on the performance of the pulverized coal power plant, and the promotion in carbon price is critical for CCS to attract the support from industry. In terms of the four CCS technologies, Oxy-CLAS comprehensively performs the best, followed by Oxy. Decrement of consumption of Ca-based sorbents is critical for Oxy-CaL to outrank Oxy.

This paper proposes a new prediction method incorporating a set of measuring models applicable to the financing efficiency of China's carbon market to quantify such market's maturity, trading risk coefficient, and financing income. Our theoretical analysis indicates that the price of carbon emission quota essentially affects the financing efficiency of carbon market. While a robust safeguard measure to obtain a respectable income from carbon market financing is the long-term average quota price exceeding the initial quota price. Empirical findings derived from China's emissions trading scheme pilots reveal that these pilots can be divided into the growth-oriented market (i.e. Guangdong whose financing capacity is always significant), the balance-oriented market (i.e. Shanghai and Hubei whose quota pricing mechanisms and the financing level of carbon markets both maturely develop), and the risk-oriented market (i.e. Beijing whose quota price runs at a high-level with an intense financing income volatility). It is therefore achieved that carbon market's maturity level and quota price volatility are both robustly explicable for different financing effects among these pilots. Our key findings show that expanding the coverage of quota trading parties, stabilizing carbon price, and promoting carbon asset management help to improve the financing efficiency of carbon market.

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.

The Chinese government is increasingly concerned about the impacts of climate change mitigation policies on social equity. Carbon pricing is an effective measure for reducing carbon emissions, but its distributional effects may exacerbate inequity among different rural and urban households. As a leading carbon pricing pilot area in China, Guangdong Province is the subject area of this paper. By using the environmentally extended input-output model and calculating the Suits index, this study aims to compare the carbon price payments of ten income-level households in Guangdong Province in 2012 and 2017. The results indicate that higher-income households need to pay more for carbon emissions. Total carbon pricing in Guangdong is slightly progressive, and the progressivity increased from 2012 to 2017. Thus, the implementation of carbon pricing may not intensify the inequality between urban and rural areas and between different income groups. Only carbon pricing on residential energy consumption is regressive, which could be compensated by revenue recycling policies. The findings of this study contribute by offering a positive theoretical basis for China’s future policies on carbon pricing such as carbon taxes.

Grandfathering and benchmarking are two typical free carbon permits allocation methods used in the carbon cap-and-trade mechanism. These emission regulations would generate carbon cost and hence make the firm's manufacturing and remanufacturing production decisions more complex. In this paper, we developed two models to contrastively evaluate the effectiveness of grandfathering and benchmarking methods on motivating the monopolist manufacturer to adopt low carbon remanufacturing practice in two periods. In the first period, the manufacturer produces completely new products without restrictions of carbon emission, while in the second period, the manufacturer can use collected returns derived from the first period to produce remanufactured products with carbon emission constrained by grandfathering or benchmarking regulation. We further analyzed the impact of carbon price, carbon emission saving per remanufactured product, and technology improvement on the production decisions. Our results indicate that the benchmarking method can more effectively motivate the manufacturer to adopt the low carbon remanufacturing production than the grandfathering method. We also find that the carbon price and carbon emission saving per remanufactured product would also affect the manufacturer's remanufacturing production decision.

Cap-and-trade and carbon tax are the two main policies to reduce carbon emissions. Different from the previous literature and practice, this paper examines the production and transportation outsourcing problems of a two-echelon supply chain under the cap-and-trade policy and joint cap-and-trade and carbon tax policy. Enterprise operations are divided into the order stage and transportation outsourcing decision stage. In the order stage, the retailer sets his order schedule for the manufacturing plant for fulfillment, and in the transportation outsourcing decision stage, the manufacturer decides the optimal freight volume based on energy consumption. Furthermore, we construct a basic model without a carbon policy and two extended models with carbon policies and the optimal solutions of the models are solved. Numerical examples are provided, and the three models are compared. The results indicate that the extended model with carbon policies is more beneficial for emissions reduction, and the effect of emissions reduction gradually becomes smooth as the carbon price increases; the joint cap-and-trade and carbon tax policy is more effective for emissions reduction, especially when the carbon price is lower. These findings will be helpful to policy makers and managers.

Governments and industry are actively seeking means of reducing global warming. The management of supply chains while taking into account the effects on the environment is an important issue. Therefore, we present newsvendor models that take into account carbon emissions, trade credit, and product recycling, in which the credit period affects the risk of default and the demand is uncertain in this paper. The objective of these models is to determine the optimal ordering quantity, credit period, and recycle price in order to maximize total profits. Three carbon emission incentives are taken into account: (1) carbon tax (2) direct cap (3) carbon cap and trade. We also incorporate risk-management methods to limit risk of default, providing a solution for each type of carbon emission incentive. Numerical analysis is used to illustrate the implementation of solutions and the degree to which system parameters affect profits and decision making. The results show that higher carbon taxes reduce recycle prices, order quantities, and total profits. Increasing the carbon cap leads to increases in recycle price, order quantity, and total profit. A higher carbon price drives down the recycle price, order quantity, and total profits.

The Covid-19 crisis has had a major impact on electricity markets, affecting power plant input and output prices. In this paper Spanish electricity and natural gas prices and international carbon prices are used to calculate the variable margin of natural gas combined cycles (NGCC), i.e. the Clean Spark Spread (CSS). The stochastic behavior of the CSS is modeled using an Ornstein-Uhlenbeck (OU) process because of its properties. The expected first semester 2020 CSS results based on the fitted model with daily 2016-2019 data, taking the end of 2019 as a starting point, are compared with the actual figures for the same period. In the first half of 2020 electricity and natural gas prices are significantly lower than expected at the end of 2019, but carbon allowance prices have decreased less in percentage terms. The monthly CSS values in the first half of 2020 are significantly lower than expected for March-May 2020, with the April value being -€4.15/WWh lower figure than the €7.16/MWh expected. This work calculates distributions of daily and monthly CSS values. This research is supported by the Basque Government through the BERC 2018–2021 programme and by the Spanish Ministry of the Economy and Competitiveness (MINECO) through BC3 María de Maeztu excellence accreditation MDM-2017-0714 . Further support is provided by the project MINECO RTI 2018-093352-B-I00 .

The “Carbon Neutral Growth from 2020″ strategy (CNG2020 strategy) has predicted the total emission reduction amount but has not arranged the special emission reduction proportion for an individual airline. In this paper, we build a DEA model to calculate the emission reduction allocation proportion and explore the impacts of a different carbon price. We select Number of Employees, Fleet Size, and Aviation Kerosene as then inputs, and set Total Revenue as the output. Then we apply BP neural network to predict the data from 2021 to 2023 based on the actual data of 27 global airlines from 2008 to 2018. We discuss the allocation proportions when the price is 8 dollars/ton, when the price is 14 dollars/ton and when the price is 20 dollars/ton. We have some findings: 1. Skyteam will have the largest average reduction allocation proportion in 2021 and 2023, while Oneworld will have the largest allocation proportion in 2022.2. The largest average ERAP in 2021 and 2023 is from Delta Air Lines, and the largest average ERAP in 2022 is from Air France-KLM.

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.

The shortage of landfill space and the environmental pollution from urbanization and industrialization have led to an increase in the recycling and reuse of construction and demolition waste (CDW). Based on the technological paradigm, this paper proposes a closed-loop supply chain (CLSC) that integrates forward and reverse logistics and has multiple centers that regulate end-of-life CDW recycling and promote new product remanufacturing. Carbon price (CP) and carbon trading (CT) policies are then innovatively applied to manage the proposed supply chain, incentivize technological CDW disposal improvements, encourage policy adherence, and ensure that the economic and environmental benefits are built into the proposed CLSC. A mixed integer nonlinear programming model (MINLP) is developed to optimize the proposed CLSC by minimizing the total costs under the two carbon policies, and a real case study from China is given. Scenario analyses under the two carbon polices and practical policy suggestions are given, and comprehensive conclusions are provided in the conclusions. It was found that with the adjustment of recycling ratio, the proposed CDW CLSC was able to minimize total costs and overall carbon emissions. Moreover, the CP policy could improve the CDW recycling ratio from 19.4% to 85.3% and reduced carbon emissions by 6393 tonnes, and the CT policy should be an incentive policy to further encourage a higher recycling ratio and lower carbon emissions.

With the rapid spread of carbon trading in the world, the interaction of carbon prices and fossil energy prices has raised growing attention, but little research has discussed their time-varying correlation and dynamic volatility spillover. This paper employs the threshold dynamic conditional correlation (DCC) generalized autoregressive conditional heteroscedasticity (GARCH) model and the full Baba, Engle, Kraft and Kroner (BEKK) GARCH model to explore these issues, for the daily data of European carbon futures prices and the three fossil energy prices (coal, natural gas and Brent oil) from January 2 2008 to September 30 2014. The results indicate that, first, there is significant unidirectional volatility spillover from coal market to carbon market and from carbon market to natural gas market, whereas there exists no significant volatility spillover between carbon market and Brent oil market. Second, carbon market and fossil energy markets have significantly positive correlation across time. Specifically, among the three fossil fuels, coal market has the highest correlation with carbon market, followed by natural gas and Brent oil markets. Finally, as for the three fossil fuels, their price decrease may have stronger impact on carbon price volatility than their price increase with the same degree, while there is asymmetric impact of carbon price increase and decrease only on Brent oil price volatility. These results may help investors to well configure their portfolios and manage their investment risks, and help emission trading installations to join in carbon market in a cost-effective way.

Existing literature on the optimal type of carbon mitigation policy and whether it is better to apply a policy mix instead of a single policy is still unclear. This paper assesses the policy effectiveness under exogenous shocks by applying a dynamic stochastic general equilibrium (DSGE) model. Comparing the differences between the carbon mitigation and economic impacts of a policy mix versus a single policy (carbon tax or carbon trading scheme), the results showed that energy demand is rigid in response to technological shocks. Therefore, there is no significant difference for the energy consumption related carbon emissions in response to the technological progress under three different policy scenarios: a carbon tax (scenario I), a carbon trading scheme (scenario II), and a carbon policy mix (scenario III). Overall, carbon emissions policy has a negative impact on the economy, compared to the other two policies, a carbon trading scheme has a relatively slight impact on the economy. A mandatory carbon price would have a more significant impact than would a market-based carbon price. The policy mix formed by a carbon tax and a carbon trading is comprehensive in terms of both price flexibility and coverage scope.

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.

Establishing a carbon market is widely regarded as an effective means of controlling global carbon emission. Purchasing carbon emission right will increase the cost of enterprises, especially for high-energy consumption industry. The volatility of carbon market will undoubtedly affect the behavior of these enterprises. It has been well recognized that there is significant interaction between the carbon market and the fossil energy (crude oil, natural gas and coal) market. This paper employs recurrence plot (RP) method and recurrence quantification analysis (RQA) method to investigate the volatility spillover between the three different energy futures markets and carbon emission market. The result shows that the volatility spillover between the coal market and carbon emission market is strongest. Based on this, in order to avoid the risk from the carbon emission market, industries should switch from coal to natural gas or oil, and this behavior will lead to the reduction of carbon emission. Industries will change their energy consumption structure to a much cleaner one which will reduce air pollutant and the emission of carbon dioxide on the realization of carbon value.

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.

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.

Carbon trading is one of the important mechanisms used to reduce carbon dioxide emissions. The increasing interest in the carbon trading market has heightened the need to decrease the prediction error of the carbon price. In this paper, a new hybrid model for carbon price forecasting is proposed, and the secondary decomposition algorithm is innovatively introduced into carbon price forecasting. First, time series data were decomposed into several intrinsic modal functions by empirical mode decomposition (EMD). Second, the first intrinsic mode function (IMF1) was further decomposed by variational mode decomposition (VMD). Then, the model input was determined by partial autocorrelation analysis (PACF). Finally, the back propagation (BP) neural network model optimized by genetic algorithm (GA) was utilized for prediction. In the empirical analysis of the Hubei market, the proposed model outperforms other comparative models. The mean absolute percentage error (MAPE), goodness of fit (R2) and root mean square error (RMSE) of the model are 1.7577%, 0.9929 and 0.5441, respectively. In the complementary cases of the Beijing and Shanghai carbon markets, the model also performs best. The results suggest that the proposed model is effective and robust and could predict carbon prices more accurately.

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.

This study explores choice of technology for sustainable operations in a port supply chain that consists of one port and one shipping company under a cap-and-trade scheme. To control emissions, the port supply chain can implement either shore power (SP) or low sulfur fuel oil (LSFO). The performances of the port supply chain when either SP or LSFO is adopted are compared in this paper. More specifically, according to relative bargaining power between the port and the shipping company, three different game models are constructed to analyze the effects of channel power on equilibrium results when either SP or LSFO is implemented. Results show that both profits and carbon emissions of supply chain using SP are lower than those using LSFO when carbon price is low, but are higher when carbon price is high. However, from the perspective of the social welfare, when both the carbon price and environmental concern are low, or both the carbon price and environmental concern are high, LSFO should be adopted. Otherwise, SP may be more attractive. Compared with the two Stackelberg games, the Nash game leads to higher profits and less emissions. The Nash game also leads to higher social welfare when environmental concern is small, but lower social welfare when environmental concern is large. These findings can provide insights for governments in formulating emission reduction strategies in port supply chain under different scenarios.

In the context of addressing climate change, the carbon emission trading scheme has become one of the main measures adopted by many countries and regions to achieve emission reduction goals. Noticing this current lack of research, based on a duopoly model, this paper quantitatively explores the impact of carbon offsetting scheme on both emission trading participants' profits and industry's output by drawing on advanced experience of carbon offsetting scheme from developed countries, such as US, Switzerland and EU, and thus provides a perspective for government to design optimal aggregate standard for carbon cap-and-trade. Results show a negative correlation between enterprises' carbon intensity and their equilibrium output in the product market, and indicate a threshold for the relative magnitude of the duopoly enterprises' carbon intensity, above which their absolute output will differ dramatically. The incorporation of carbon offsetting scheme into a non-offset quota trading scheme will reduce its equilibrium carbon price, thereby mitigate its negative impact on industry's total output in the product market by an either linear or quadratic form, depending on the design for the proportion ceiling of offsetting quota.