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Special Issue "Carbon Emission Reduction—Carbon Tax, Carbon Trading, and Carbon Offset"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Energy and Environment".

Deadline for manuscript submissions: 30 November 2019.

Special Issue Editor

Guest Editor
Prof. Dr. Wen-Hsien Tsai

Distinguished Professor of Information & Accounting Systems, Department of Business Administration, National Central University, Jhongli, Taoyuan 32001, Taiwan
Website | E-Mail
Interests: sustainability; green production decision model; Industry 4.0; corporate social responsibility (CSR); activity-based costing (ABC); enterprise resource planning (ERP); carbon emission cost; energy saving and carbon emission reduction; international financial reporting standards (IFRS)

Special Issue Information

Dear Colleagues,

The Paris Agreement was signed by 195 nations in December 2015 to strengthen the global response to the threat of climate change, following the 1992 United Nations Framework Convention on Climate Change (UNFCC) and the 1997 Kyoto Protocol. In Article 2 of the Paris Agreement, the increase in the global average temperature is anticipated to be held to well below 2 °C above pre-industrial levels, and efforts are being employed to limit the temperature increase to 1.5 °C. It is estimated that about 72% of the totally emitted greenhouse gases is carbon dioxide (CO2), 18% methane, and 9% nitrous oxide. Therefore, carbon dioxide (CO2) emissions (or carbon emissions) are the most important cause of global warming. The United Nations has made efforts to reduce greenhouse gas emissions or mitigate their effect. In Article 6 of the Paris Agreement, three cooperative approaches that countries can take in attaining the goal of their carbon emission reduction are described, including direct bilateral cooperation, new sustainable development mechanisms, and non-market-based approaches.

The World Bank stated that there are some incentives which have been created to encourage carbon emission reduction, such as the removal of fossil fuels subsidies, the introduction of carbon pricing, the increase of energy efficiency standards, and the implementation of auctions for the lowest-cost renewable energy. Among these, carbon pricing refers to charges those who emit carbon dioxide (CO2) for their emissions, including carbon taxes, emissions trading systems (ETSs), offset mechanisms, results-based climate finance (RBCF), and so on. In view of the urgent need for carbon emission reduction, this Special Issue will focus the on the discussion of carbon tax, carbon trading, and carbon offset.

Carbon tax is a tax on energy sources which emit carbon dioxide. It is a pollution tax and a form of carbon pricing. The objective of a carbon tax is to reduce the harmful and unfavorable levels of carbon dioxide emissions, thereby decelerating climate change and its negative effects on the environment and human health. Carbon tax also can prompt companies to find more efficient ways to manufacture their products or deliver their services. Generally, carbon tax is determined by the carbon tax rate and the quantity of carbon emissions of a company in its manufacturing processes, and it is represented as the amount paid for every ton of greenhouse gas released into the atmosphere. However, carbon tax also will have some disadvantages, such as imposing expensive administration costs for businesses, prompting them to move their operations to “pollution havens”, and so on.

Carbon trading is another form of carbon pricing under cap-and-trade systems. Cap-and-trade is one method for regulating and ultimately reducing the amount of carbon emissions. The government sets a cap on carbon emissions for the whole country, then limits the amount of carbon dioxide that companies are allowed to release. A company that can more efficiently reduce carbon emissions can sell any extra permits in the emission market to companies that cannot easily afford to reduce carbon emission. Thus, carbon trading markets are set up. The number of emissions trading systems around the world is increasing. In addition to the EU emissions trading system (EU ETS), national or subnational systems are already in operation or under development in Canada, China, Japan, New Zealand, South Korea, Switzerland, and the United States.

A carbon offset is a reduction in emissions of carbon dioxide or greenhouse gases made in order to compensate for or to offset an emission made elsewhere. One ton of carbon offset represents the reduction of one ton of carbon dioxide or its equivalent in other greenhouse gases. There are two markets for carbon offsets: (1) The larger compliance market, where companies, governments, or other entities buy carbon offsets in order to comply with caps on the total amount of carbon dioxide they are allowed to emit; and (2) the smaller voluntary market, where individuals, companies, or governments purchase carbon offsets to mitigate their own greenhouse gas emissions from transportation, electricity use, and other sources. Carbon offset usually supports projects that reduce the emission of greenhouse gases in the short- or long-term. A common project type is renewable energy, such as wind farms, biomass energy, or hydroelectric dams. Others include energy efficiency projects, the destruction of industrial pollutants or agricultural byproducts, the destruction of landfill methane, LULUCF (land use, land-use change, and forestry), REDD (reducing emissions from deforestation and forest degradation), and so on.

In view of the urgent need for carbon emission reduction, we would like to invite researchers and professionals from universities, enterprises, and governmental units to share new ideas, innovations, trends, and experiences concerning the related issues of carbon tax, carbon trading, carbon offset, and other related methods of carbon emission reduction. Both original research articles as well as review articles are welcome.

Prof. Dr. Wen-Hsien Tsai
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • climate change
  • global warming
  • carbon emission
  • direct carbon emission
  • indirect carbon emission
  • carbon footprint
  • greenhouse gas (GHG)
  • carbon emission reduction
  • greenhouse gas reduction
  • carbon emission cost analysis
  • quota decline scheme
  • computable general equilibrium (CGE)
  • certified emission reductions (CERs)
  • emission reduction units (ERUs)
  • carbon pricing
  • internal carbon pricing
  • carbon tax
  • energy tax
  • gasoline tax
  • decarbonization
  • cap-and-trade
  • emission permit
  • emission allowances
  • allowance allocation mechanism
  • carbon (emission) trading
  • carbon trading market
  • international emission trading (IET)
  • personal carbon trading
  • carbon credit
  • tradable renewable credit
  • emission trading schemes (ETS)
  • European Union emission trading schemes (EU ETS)
  • carbon offset
  • renewable energy project
  • energy efficiency project
  • low-carbon energy
  • zero carbon energy
  • zero emission vehicle
  • vehicle electrification
  • zero emission building
  • methane collection and combustion
  • forestry project
  • land use, land-use change and forestry (LULUCF)
  • reforestation
  • afforestation
  • reducing emissions from deforestation and forest degradation (REDD)
  • REDD+
  • carbon retirement
  • carbon capture and storage
  • forest carbon sinks
  • CO2 recycling
  • carbon leakage
  • enterprise carbon accounting

Published Papers (9 papers)

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Research

Open AccessArticle
Spatial-Temporal Characteristics of the Driving Factors of Agricultural Carbon Emissions: Empirical Evidence from Fujian, China
Energies 2019, 12(16), 3102; https://doi.org/10.3390/en12163102
Received: 12 July 2019 / Revised: 11 August 2019 / Accepted: 11 August 2019 / Published: 13 August 2019
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Abstract
With the development of agricultural modernization, the carbon emissions caused by the agricultural sector have attracted academic and practitioners’ circles’ attention. This research selected the typical agricultural development province in China, Fujian, as the research object. Based on the carbon emission sources of [...] Read more.
With the development of agricultural modernization, the carbon emissions caused by the agricultural sector have attracted academic and practitioners’ circles’ attention. This research selected the typical agricultural development province in China, Fujian, as the research object. Based on the carbon emission sources of five main aspects in agricultural production, this paper applied the latest carbon emission coefficients released by Intergovernmental Panel on Climate Change of the UN (IPCC) and World Resources Institute (WRI), then used the ordered weighted aggregation (OWA) operator to remeasure agricultural carbon emissions in Fujian from 2008–2017. The results showed that the amount of agricultural carbon emissions in Fujian was 5541.95 × 103 tonnes by 2017, which means the average amount of agricultural carbon emissions in 2017 was 615.78 × 103 tonnes, with a decrease of 13.13% compared with that in 2008. In terms of spatial distribution, agricultural carbon emissions in the eastern coastal areas were less than those in the inland regions. Among them, the highest agricultural carbon emissions were in Zhangzhou, Nanping, and Sanming, while the lowest were in Xiamen, Putian, and Ningde. In addition, this paper selected six influencing variables, the research and development intensity, the proportion of agricultural labor force, the added value of agriculture, the agricultural industrial structure, the per capita disposable income of rural residents, and per capita arable land area, to clarify further the impacts on agricultural carbon emissions. Finally, geographically- and temporally-weighted regression (GTWR) was used to measure the direction and degree of the influences of factors on agricultural carbon emission. The conclusion showed that the regression coefficients of each selected factor in cities were positive or negative, which indicated that the impacts on agricultural carbon emission had the characteristics of geospatial nonstationarity. Full article
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Open AccessArticle
The Efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand: Adapting the SEM-VARIMAX Model
Energies 2019, 12(16), 3092; https://doi.org/10.3390/en12163092
Received: 25 June 2019 / Revised: 3 August 2019 / Accepted: 9 August 2019 / Published: 12 August 2019
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Abstract
This research aims to predict the efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand for the next 17 years (2020–2036) and analyze the relationships among causal factors by applying a structural equation modeling/vector autoregressive model with exogenous [...] Read more.
This research aims to predict the efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand for the next 17 years (2020–2036) and analyze the relationships among causal factors by applying a structural equation modeling/vector autoregressive model with exogenous variables (SEM-VARIMAX Model). This model is effective for analyzing relationships among causal factors and optimizing future forecasting. It can be applied to contexts in different sectors, which distinguishes it from other previous models. Furthermore, this model ensures the absence of heteroskedasticity, multicollinearity, and autocorrelation. In fact, it meets all the standards of goodness of fit. Therefore, it is suitable for use as a tool for decision-making and planning long-term national strategies. With the implementation of the Sustainable Development Policy for Energy Consumption under Environmental Law ( S . D . E L ) , the forecast results derived from the SEM-VARIMAX Model indicate a continuously high change in energy consumption from 2020 to 2036the change exceeds the rate determined by the government. In addition, energy consumption is predicted to have an increased growth rate of up to 185.66% (2036/2020), which is about 397.08 ktoe (2036). The change is primarily influenced by a causal relationship that contains latent variables, namely, the economic factor ( E C O N ) , social factor ( S O C I ) , and environmental factor ( E N V I ) . The performance of the SEM-VARIMAX Model was tested, and the model produced a mean absolute percentage error (MAPE) of 1.06% and a root-mean-square error (RMSE) of 1.19%. A comparison of these results with those of other models, including the multiple linear regression model (MLR), back-propagation neural network (BP model), grey model, artificial neural natural model (ANN model), and the autoregressive integrated moving average model (ARIMA model), indicates that the SEM-VARIMAX model fits and is appropriate for long-term national policy formulation in various contexts in Thailand. This study’s results further indicate the low efficiency of Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand. The predicted result for energy consumption in 2036 is greater than the government-established goal for consumption of no greater than 251.05 ktoe. Full article
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Open AccessArticle
Analysis of Influencing Factors and Trend Forecast of Carbon Emission from Energy Consumption in China Based on Expanded STIRPAT Model
Energies 2019, 12(16), 3054; https://doi.org/10.3390/en12163054
Received: 1 July 2019 / Revised: 4 August 2019 / Accepted: 5 August 2019 / Published: 8 August 2019
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Abstract
With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission [...] Read more.
With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission reduction. This paper uses the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and uses it as a dependent variable to analyze the influencing factors. In this paper, five factors, total population, per capita GDP (Gross Domestic Product), urbanization level, primary energy consumption structure, technology level, and industrial structure are selected as the influencing factors of carbon emissions. Based on the expanded STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the influencing degree of different factors on carbon emissions of energy consumption is analyzed. The results show that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level. On the basis of the above research, the carbon emissions of China′s energy consumption in the future are predicted under eight different scenarios. The results show that, when the population and economy keep a low growth rate, while improving the technology level can effectively control carbon emissions from energy consumption, China′s carbon emissions from energy consumption will reach 302.82 million tons in 2020. Full article
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Open AccessArticle
A Research on the Factors Influencing Carbon Emission of Transportation Industry in “the Belt and Road Initiative” Countries Based on Panel Data
Energies 2019, 12(12), 2405; https://doi.org/10.3390/en12122405
Received: 13 April 2019 / Revised: 17 June 2019 / Accepted: 19 June 2019 / Published: 22 June 2019
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Abstract
Carbon emissions in countries in the “Belt and Road Initiative (BRI)” account for more than half of the world’s total volume. According to the international energy agency report, the world transportation industry carbon emissions in 2015 came second on the list for the [...] Read more.
Carbon emissions in countries in the “Belt and Road Initiative (BRI)” account for more than half of the world’s total volume. According to the international energy agency report, the world transportation industry carbon emissions in 2015 came second on the list for the proportion of global carbon emissions across all industries, accounting for 23.96% of the total. Along with the advancement of the BRI construction, transportation industry carbon emissions will continue their rapid growth. Therefore, studying the factors affecting the carbon emissions of the transportation industry in countries in the BRI is conducive to the formulation of policies to control carbon emissions. In this paper, the CO2 emissions of the transportation industry in countries in the BRI line from 2005 to 2015 were measured, and then the influencing factors of 57 countries in the BRI were analyzed by using the panel data model. The results show that per capita GDP, urbanization level, and energy consumption structure have positive effects on the carbon emissions of transportation industry, while technology level and trade openness have negative effects on carbon emissions of the transportation industry. Therefore, in order to effectively control the carbon emissions of the transportation industry in the BRI countries, it is necessary to reasonably control the transportation industry carbon emissions caused by urbanization, optimize the energy consumption structure of the transportation industry, optimize the structure of the transportation industry, and improve the openness of trade and the technical level of the BRI countries. Full article
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Open AccessArticle
Optimization in the Stripping Process of CO2 Gas Using Mixed Amines
Energies 2019, 12(11), 2202; https://doi.org/10.3390/en12112202
Received: 30 April 2019 / Revised: 1 June 2019 / Accepted: 5 June 2019 / Published: 10 June 2019
Cited by 1 | PDF Full-text (3477 KB) | HTML Full-text | XML Full-text
Abstract
The aim of this work was to explore the effects of variables on the heat of regeneration, the stripping efficiency, the stripping rate, the steam generation rate, and the stripping factor. The Taguchi method was used for the experimental design. The process variables [...] Read more.
The aim of this work was to explore the effects of variables on the heat of regeneration, the stripping efficiency, the stripping rate, the steam generation rate, and the stripping factor. The Taguchi method was used for the experimental design. The process variables were the CO2 loading (A), the reboiler temperature (B), the solvent flow rate (C), and the concentration of the solvent (monoethanolamine (MEA) + 2-amino-2-methyl-1-propanol (AMP)) (D), which each had three levels. The stripping efficiency (E), stripping rate ( m ˙ CO 2 ), stripping factor (β), and heat of regeneration (Q) were determined by the mass and energy balances under a steady-state condition. Using signal/noise (S/N) analysis, the sequence of importance of the parameters and the optimum conditions were obtained, and the optimum operating conditions were further validated. The results showed that E was in the range of 20.98–55.69%; m ˙ CO 2 was in the range of 5.57 × 10−5–4.03 × 10−4 kg/s, and Q was in the range of 5.52–18.94 GJ/t. In addition, the S/N ratio analysis showed that the parameter sequence of importance as a whole was A > B > D > C, while the optimum conditions were A3B3C1D1, A3B3C3D2, and A3B2C2D2, for E, m ˙ CO 2 , and Q, respectively. Verifications were also performed and were found to satisfy the optimum conditions. Finally, the correlation equations that were obtained were discussed and an operating policy was discovered. Full article
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Open AccessArticle
An Empirical Study on Low Emission Taxiing Path Optimization of Aircrafts on Airport Surfaces from the Perspective of Reducing Carbon Emissions
Energies 2019, 12(9), 1649; https://doi.org/10.3390/en12091649
Received: 8 April 2019 / Revised: 25 April 2019 / Accepted: 28 April 2019 / Published: 30 April 2019
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Abstract
Aircraft emissions are the main cause of airport air pollution. One of the keys to achieving airport energy conservation and emission reduction is to optimize aircraft taxiing paths. The traditional optimization method based on the shortest taxi time is to model the aircraft [...] Read more.
Aircraft emissions are the main cause of airport air pollution. One of the keys to achieving airport energy conservation and emission reduction is to optimize aircraft taxiing paths. The traditional optimization method based on the shortest taxi time is to model the aircraft under the assumption of uniform speed taxiing. Although it is easy to solve, it does not take into account the change of the velocity profile when the aircraft turns. In view of this, this paper comprehensively considered the aircraft’s taxiing distance, the number of large steering times and collision avoidance in the taxi, and established a path optimization model for aircraft taxiing at airport surface with the shortest total taxi time as the target. The genetic algorithm was used to solve the model. The experimental results show that the total fuel consumption and emissions of the aircraft are reduced by 35% and 46%, respectively, before optimization, and the taxi time is greatly reduced, which effectively avoids the taxiing conflict and reduces the pollutant emissions during the taxiing phase. Compared with traditional optimization methods that do not consider turning factors, energy saving and emission reduction effects are more significant. The proposed method is faster than other complex algorithms considering multiple factors, and has higher practical application value. It is expected to be applied in the more accurate airport surface real-time running trajectory optimization in the future. Future research will increase the actual interference factors of the airport, comprehensively analyze the actual situation of the airport’s inbound and outbound flights, dynamically adjust the taxiing path of the aircraft and maintain the real-time performance of the system, and further optimize the algorithm to improve the performance of the algorithm. Full article
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Open AccessArticle
Research on the Impact of Various Emission Reduction Policies on China’s Iron and Steel Industry Production and Economic Level under the Carbon Trading Mechanism
Energies 2019, 12(9), 1624; https://doi.org/10.3390/en12091624
Received: 3 April 2019 / Revised: 21 April 2019 / Accepted: 23 April 2019 / Published: 29 April 2019
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Abstract
To study the emission reduction policies’ impact on the production and economic level of the steel industry, this paper constructs a two-stage dynamic game model and analyzes various emission reduction policies’ impact on the steel industry and enterprises. New results are observed in [...] Read more.
To study the emission reduction policies’ impact on the production and economic level of the steel industry, this paper constructs a two-stage dynamic game model and analyzes various emission reduction policies’ impact on the steel industry and enterprises. New results are observed in the study: (1) With the increasing emission reduction target (15%–30%) and carbon quota trading price (12.65–137.59 Yuan), social welfare and producer surplus show an increasing trend and emission macro losses show a decreasing trend. (2) Enterprises’ reduction ranges in northwestern and southwestern regions are much higher than that of the other regions; the northeastern enterprise has the smallest reductions range. (3) When the market is balanced (0.8543–0.9320 billion tons), the steel output has decreased and the polarization in various regions has been alleviated to some extent. The model is the abstraction and assumption of reality, which makes the results have some deviations. However, these will provide references to formulate reasonable emissions reduction and production targets. In addition, the government needs to consider the whole and regional balance and carbon trading benchmark value when deciding the implementation of a single or mixed policy. Future research will be more closely linked to national policies and gradually extended to other high-energy industries. Full article
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Open AccessArticle
Trade Openness and Carbon Leakage: Empirical Evidence from China’s Industrial Sector
Energies 2019, 12(6), 1101; https://doi.org/10.3390/en12061101
Received: 11 February 2019 / Revised: 4 March 2019 / Accepted: 5 March 2019 / Published: 21 March 2019
Cited by 1 | PDF Full-text (689 KB) | HTML Full-text | XML Full-text
Abstract
China is a large import and export economy in global terms, and the carbon dioxide emissions and carbon leakage arising from trade have great significance for China’s foreign trade and its economy. On the basis of trade data for China’s 20 industrial sectors, [...] Read more.
China is a large import and export economy in global terms, and the carbon dioxide emissions and carbon leakage arising from trade have great significance for China’s foreign trade and its economy. On the basis of trade data for China’s 20 industrial sectors, we first built a panel data model to test the effect of trade on carbon dioxide emissions and the presence of carbon leakage for all industrial sectors. Second, we derived a single-region input–output model for open economies based on the industrial sectors’ diversity and carbon dioxide emissions, and performed an empirical test. We estimated the net carbon intensity embodied in export, which is 0.237tCO2/ten thousand RMB, to divide all sectors (ACSs) into high-carbon sectors (HCSs) and low-carbon sectors (LCSs). The results show that higher trade openness leads to a reduction in the intensity of CO2 emissions and gross emissions and that there are obvious structural differences in different sectors with different carbon emission intensity. The coefficient of trade openness for LCSs is −0.073 and is statistically significant at the 1% level, so higher trade openness for LCSs leads to a reduction in the CO2 emissions intensity. However, the coefficient for HCSs is 0.117 and is statistically significant at the 10% level, indicating that higher trade openness increases the CO2 emissions’ intensity for HCSs. The difference is that higher trade openness in LCSs can help reduce the CO2 emissions’ intensity without the problem of carbon leakage and with the existence of the environmental Kuznets curve (EKC), whereas there is no EKC for HCSs and carbon leakage may happen. We introduced dummy variables and found that a “pollution haven” effect exists in HCSs. The test results in HCSs and LCSs are exactly the opposite of each other, which shows that the carbon leakage of ACSs cannot be determined. The message that can be drawn for policy makers is that China does not need to worry about the adverse impact on the environment of trade opening up and should, in fact, increase the opening up of trade, while becoming acclimatized to environmental regulation of a new trade mode and new standards. This will help amplify the favorable impact of trade opening up on the environment and improve China’s international reputation. The policies related to trade should encourage structural adjustment between the sectors via the formulation of differential policies and impose a restraint on sectors that have high levels of CO2 emissions embodied in export. Full article
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Open AccessArticle
Emission-Intensity-Based Carbon Tax and Its Impact on Generation Self-Scheduling
Energies 2019, 12(5), 777; https://doi.org/10.3390/en12050777
Received: 26 January 2019 / Revised: 15 February 2019 / Accepted: 21 February 2019 / Published: 26 February 2019
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Abstract
We propose an emission-intensity-based carbon-tax policy for the electric-power industry and investigate the impact of the policy on thermal generation self-scheduling in a deregulated electricity market. The carbon-tax policy is designed to take a variable tax rate that increases stepwise with the increase [...] Read more.
We propose an emission-intensity-based carbon-tax policy for the electric-power industry and investigate the impact of the policy on thermal generation self-scheduling in a deregulated electricity market. The carbon-tax policy is designed to take a variable tax rate that increases stepwise with the increase of generation emission intensity. By introducing a step function to express the variable tax rate, we formulate the generation self-scheduling problem under the proposed carbon-tax policy as a mixed integer nonlinear programming model. The objective function is to maximize total generation profits, which are determined by generation revenue and the levied carbon tax over the scheduling horizon. To solve the problem, a decomposition algorithm is developed where the variable tax rate is transformed into a pure integer linear formulation and the resulting problem is decomposed into multiple generation self-scheduling problems with a constant tax rate and emission-intensity constraints. Numerical results demonstrate that the proposed decomposition algorithm can solve the considered problem in a reasonable time and indicate that the proposed carbon-tax policy can enhance the incentive for generation companies to invest in low-carbon generation capacity. Full article
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