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8 pages, 171 KiB

Open AccessEditorial

Modeling and Simulation of Carbon Emission-Related Issues

by Wen-Hsien Tsai

Energies 2019, 12(13), 2531; https://doi.org/10.3390/en12132531 - 1 Jul 2019

Cited by 5 | Viewed by 3076

Abstract

According to the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report in 2013 (IPCC, 2013) [...] Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

Research

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19 pages, 412 KiB

Open AccessArticle

Evaluation of the CO₂ Emissions Reduction Potential of Li-ion Batteries in Ship Power Systems

by César O. Peralta P., Giovani T. T. Vieira, Simon Meunier, Rodrigo J. Vale, Mauricio B. C. Salles and Bruno S. Carmo

Energies 2019, 12(3), 375; https://doi.org/10.3390/en12030375 - 24 Jan 2019

Cited by 27 | Viewed by 8013

Abstract

Greenhouse gas emissions are one of the most critical worldwide concerns, and multiple efforts are being proposed to reduce these emissions. Shipping represents around 2% of global CO

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emissions. Since ship power systems have a high dependence on fossil fuels, hybrid systems [...] Read more.

Greenhouse gas emissions are one of the most critical worldwide concerns, and multiple efforts are being proposed to reduce these emissions. Shipping represents around 2% of global CO

_{2}

emissions. Since ship power systems have a high dependence on fossil fuels, hybrid systems using diesel generators and batteries are becoming an interesting solution to reduce CO

_{2}

emissions. In this article, we analyze the potential implementation of Li-ion batteries in a platform supply vessel system through simulations using HOMER software (Hybrid Optimization Model for Multiple Energy Resources). We evaluate the impact of battery characteristics such as round trip efficiency, rated power, and energy capacity. We also evaluate the potential CO

_{2}

emissions reduction that could be achieved with two of the most common types of Li-ion batteries (lithium titanate, lithium iron phosphate). Furthermore, we consider that the Li-ion batteries are installed in a 20 ft container. Results indicate that the lithium iron phosphate battery has a better performance, even though the difference between both technologies is lower than 1% of total emissions. We also analyze the potential emissions reduction for different parts of a mission to an offshore platform for different configurations of the ship power system. The most significant potential CO

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emissions reduction among the analyzed cases is 8.7% of the total emissions, and it is achieved by the configuration including the main and auxiliary diesel engines as well as batteries. Finally, we present managerial implications of these results for both companies operating ships and ship building companies. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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13 pages, 866 KiB

Open AccessArticle

Environmental Impact and Carbon Footprint Assessment of Taiwanese Agricultural Products: A Case Study on Taiwanese Dongshan Tea

by Allen H. Hu, Chia-Hsiang Chen, Lance Hongwei Huang, Ming-Hsiu Chung, Yi-Chen Lan and Zhonghua Chen

Energies 2019, 12(1), 138; https://doi.org/10.3390/en12010138 - 1 Jan 2019

Cited by 18 | Viewed by 7878

Abstract

Climate change is an important global environmental threat. Agriculture aggravates climate change by increasing greenhouse gas (GHG) emissions, and in response, climate change reduces agricultural productivity. Consequently, the modern agricultural development mode has progressively transformed into a kind of sustainable development mode. This [...] Read more.

Climate change is an important global environmental threat. Agriculture aggravates climate change by increasing greenhouse gas (GHG) emissions, and in response, climate change reduces agricultural productivity. Consequently, the modern agricultural development mode has progressively transformed into a kind of sustainable development mode. This study aimed to determine the environmental impact and carbon footprint of Dongshan tea from Yilan County. Environmental impact was assessed with use of SimaPro version 8.0.2 and IMPACT2002+. Results showed that climate change has the largest impact upon it in general, followed by human health, natural resources, and ecosystem quality. Furthermore, with use of the IPCC 2007 100a method for carbon footprint of products (CFP), conventional tea was found to have a CFP of 7.035 kgCO₂-e, and its main contributors are the raw material (35.15%) and consumer use (45.58%) phases. From this case study, we found that the hotspots of the life cycle of environmental impact of Taiwanese tea mainly come from fertilizer input during the raw material phase, electricity use during manufacturing, and electricity use during water boiling in the consumer use phase (which contributes the largest impact). We propose the ways for consumers to use of highly efficient boiling water facilities and heating preservation, and the government must market the use of organic fertilizers in the national policy subsidies, and farmers have to prudent use of fertilizers and promote the use of local raw fertilizers, and engagement in direct sales for reducing the environmental impacts and costs of agricultural products and thus advancing sustainable agriculture development. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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17 pages, 1948 KiB

Open AccessArticle

Ethylene Supply in a Fluid Context: Implications of Shale Gas and Climate Change

by Gillian Foster

Energies 2018, 11(11), 2967; https://doi.org/10.3390/en11112967 - 1 Nov 2018

Cited by 6 | Viewed by 3371

Abstract

The recent advent of shale gas in the U.S. has redefined the economics of ethylene manufacturing globally, causing a shift towards low-cost U.S. production due to natural gas feedstock, while reinforcing the industry’s reliance on fossil fuels. At the same time, the global [...] Read more.

The recent advent of shale gas in the U.S. has redefined the economics of ethylene manufacturing globally, causing a shift towards low-cost U.S. production due to natural gas feedstock, while reinforcing the industry’s reliance on fossil fuels. At the same time, the global climate change crisis compels a transition to a low-carbon economy. These two influencing factors are complex, contested, and uncertain. This paper projects the United States’ (U.S.) future ethylene supply in the context of two megatrends: the natural gas surge and global climate change. The analysis models the future U.S. supply of ethylene in 2050 based on plausible socio-economic scenarios in response to climate change mitigation and adaptation pathways as well as a range of natural gas feedstock prices. This Vector Error Correction Model explores the relationships between these variables. The results show that ethylene supply increased in nearly all modeled scenarios. A combination of lower population growth, lower consumption, and higher natural gas prices reduced ethylene supply by 2050. In most cases, forecasted CO₂ emissions from ethylene production rose. This is the first study to project future ethylene supply to go beyond the price of feedstocks and include socio-economic variables relevant to climate change mitigation and adaptation. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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32 pages, 1982 KiB

Open AccessArticle

Can China Achieve the 2020 and 2030 Carbon Intensity Targets through Energy Structure Adjustment?

by Ying Wang, Peipei Shang, Lichun He, Yingchun Zhang and Dandan Liu

Energies 2018, 11(10), 2721; https://doi.org/10.3390/en11102721 - 11 Oct 2018

Cited by 17 | Viewed by 3060

Abstract

To mitigate global warming, the Chinese government has successively set carbon intensity targets for 2020 and 2030. Energy restructuring is critical for achieving these targets. In this paper, a combined forecasting model is utilized to predict primary energy consumption in China. Subsequently, the [...] Read more.

To mitigate global warming, the Chinese government has successively set carbon intensity targets for 2020 and 2030. Energy restructuring is critical for achieving these targets. In this paper, a combined forecasting model is utilized to predict primary energy consumption in China. Subsequently, the Markov model and non-linear programming model are used to forecast China’s energy structure in 2020 and 2030 in three scenarios. Carbon intensities were forecasted by combining primary energy consumption, energy structure and economic forecasting. Finally, this paper analyzes the contribution potential of energy structure optimization in each scenario. Our main research conclusions are that in 2020, the optimal energy structure will enable China to achieve its carbon intensity target under the conditions of the unconstrained scenario, policy-constrained scenario and minimum external costs of carbon emissions scenario. Under the three scenarios, the carbon intensity will decrease by 42.39%, 43.74%, and 42.67%, respectively, relative to 2005 levels. However, in 2030, energy structure optimization cannot fully achieve China’s carbon intensity target under any of the three scenarios. It is necessary to undertake other types of energy-saving emission reduction measures. Thus, our paper concludes with some policy suggestions to further mitigate China’s carbon intensities. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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15 pages, 2454 KiB

Open AccessArticle

A Study on the Strategy for Departure Aircraft Pushback Control from the Perspective of Reducing Carbon Emissions

by Xinhua Zhu, Nan Li, Yu Sun, Hongfei Zhang, Kai Wang and Sang-Bing Tsai

Energies 2018, 11(9), 2473; https://doi.org/10.3390/en11092473 - 17 Sep 2018

Cited by 9 | Viewed by 3912

Abstract

In order to reduce the taxiing time of departing aircraft and reduce the fuel consumption and exhaust emissions of the aircraft, Shanghai Hongqiao Airport was taken as an example to study the control strategy for aircraft departure. In this paper, the influence of [...] Read more.

In order to reduce the taxiing time of departing aircraft and reduce the fuel consumption and exhaust emissions of the aircraft, Shanghai Hongqiao Airport was taken as an example to study the control strategy for aircraft departure. In this paper, the influence of the number of departure aircraft on the runway utilization rate, the takeoff rate, and the departure rate of flight departures under the conditions of airport runway capacity constraints are studied. The influence of factors, such as the number of departure aircraft, the gate position of the aircraft, and the configuration of airport arrival and departure runways, on the aircraft taxiing time for departure is analyzed. Based on a multivariate linear regression equation, a time prediction model of aircraft departure taxiing time is established. The fuel consumption and pollutant emissions of aircraft are calculated. The experimental results show that, without reducing the utilization rate of the runway and the departure rate of flights, implementing a reasonable pushback number for control of departing aircraft during busy hours can reduce the departure taxiing time of aircraft by nearly 32%, effectively reducing the fuel consumption and pollutant emissions during taxiing on the airport surface. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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15 pages, 1892 KiB

Open AccessArticle

Life Cycle Assessment of a Buoy-Rope-Drum Wave Energy Converter

by Qiang Zhai, Linsen Zhu and Shizhou Lu

Energies 2018, 11(9), 2432; https://doi.org/10.3390/en11092432 - 13 Sep 2018

Cited by 22 | Viewed by 4778

Abstract

This study presents a life cycle assessment (LCA) study for a buoy-rope-drum (BRD) wave energy converter (WEC), so as to understand the environmental performance of the BRD WEC by eco-labeling its life cycle stages and processes. The BRD WEC was developed by a [...] Read more.

This study presents a life cycle assessment (LCA) study for a buoy-rope-drum (BRD) wave energy converter (WEC), so as to understand the environmental performance of the BRD WEC by eco-labeling its life cycle stages and processes. The BRD WEC was developed by a research group at Shandong University (Weihai). The WEC consists of three main functional modules including buoy, generator and mooring modules. The designed rated power capacity is 10 kW. The LCA modeling is based on data collected from actual design, prototype manufacturing, installation and onsite sea test. Life cycle inventory (LCI) analysis and life cycle impact analysis (LCIA) were conducted. The analyses show that the most significant environmental impact contributor is identified to be the manufacturing stage of the BRD WEC due to consumption of energy and materials. Potential improvement approaches are proposed in the discussion. The LCI and LCIA assessment results are then benchmarked with results from reported LCA studies of other WECs, tidal energy converters, as well as offshore wind and solar PV systems. This study presents the energy and carbon intensities and paybacks with 387 kJ/kWh, 89 gCO₂/kWh, 26 months and 23 months respectively. The results show that the energy and carbon intensities of the BRD WEC are slightly larger than, however comparable, in comparison with the referenced WECs, tidal, offshore wind and solar PV systems. A sensitivity analysis was carried out by varying the capacity factor from 20–50%. The energy and carbon intensities could reach as much as 968 kJ/kWh and 222 gCO₂/kWh respectively while the capacity factor decreasing to 20%. Limitations for this study and scope of future work are discussed in the conclusion. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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26 pages, 4384 KiB

Open AccessArticle

Influencing Factors and Scenario Forecasts of Carbon Emissions of the Chinese Power Industry: Based on a Generalized Divisia Index Model and Monte Carlo Simulation

by Lin Zhu, Lichun He, Peipei Shang, Yingchun Zhang and Xiaojun Ma

Energies 2018, 11(9), 2398; https://doi.org/10.3390/en11092398 - 11 Sep 2018

Cited by 38 | Viewed by 4134

Abstract

The power industry is the industry with the most direct uses of fossil fuels in China and is one of China’s main carbon industries. A comprehensive and accurate analysis of the impacts of carbon emissions by the power industry can reveal the potential [...] Read more.

The power industry is the industry with the most direct uses of fossil fuels in China and is one of China’s main carbon industries. A comprehensive and accurate analysis of the impacts of carbon emissions by the power industry can reveal the potential for carbon emissions reductions in the power industry to achieve China’s emissions reduction targets. The main contribution of this paper is the use of a Generalized Divisia Index Model for the first time to factorize the change of carbon emissions in China’s power industry from 2000 to 2015, and gives full consideration to the influence of the economy, population, and energy consumption on the carbon emissions. At the same time, the Monte Carlo method is first used to predict the carbon emissions of the power industry from 2017 to 2030 under three different scenarios. The results show that the output scale is the most important factor leading to an increase in carbon emissions in China’s power industry from 2000 to 2015, followed by the energy consumption scale and population size. Energy intensity levels have always promoted carbon emissions reduction in the power industry, where energy intensity and carbon intensity effects of energy consumption have great potential to mitigate carbon levels. By setting the main factors affecting carbon emissions in the future three scenarios, this paper predicts the carbon emissions of China’s power industry from 2017 to 2030. Under the baseline scenario, the maximum probability range of the potential annual growth rate of carbon emissions by the power industry in China from 2017 to 2030 is 1.9–2.2%. Under the low carbon scenario and technological breakthrough scenario, carbon emissions in China’s power industry continue to decline from 2017 to 2030. The maximum probability range of the potential annual drop rate are measured at 1.6–2.1% and 1.9–2.4%, respectively. The results of this study show that China’s power industry still has great potential to reduce carbon emissions. In the future, the development of carbon emissions reduction in the power industry should focus on the innovation and development of energy saving and emissions reduction technology on the premise of further optimizing the energy structure and adhering to the low-carbon road. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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26 pages, 2308 KiB

Open AccessArticle

It is Worth Pondering Whether a Carbon Tax is Suitable for China’s Agricultural-Related Sectors

by Weiguo Fan, Zhicheng Gao, Nan Chen, Hejie Wei, Zihan Xu, Nachuan Lu, Xuechao Wang, Peng Zhang, Jiahui Ren, Sergio Ulgiati and Xiaobin Dong

Energies 2018, 11(9), 2296; https://doi.org/10.3390/en11092296 - 31 Aug 2018

Cited by 5 | Viewed by 3540

Abstract

Studying the characteristics, trends, and evolution of carbon emissions in agricultural related sectors is of great significance for rational formulation of carbon emission reduction policies. However, as an important carbon emission reduction policy, carbon tax has been controversial over whether or not it [...] Read more.

Studying the characteristics, trends, and evolution of carbon emissions in agricultural related sectors is of great significance for rational formulation of carbon emission reduction policies. However, as an important carbon emission reduction policy, carbon tax has been controversial over whether or not it should be levied on China. Based on this consideration, this paper takes China’s agricultural related sectors as an example and analyzes the degree of carbon tax on macro-environment, macroeconomy, and agricultural sectors during the period 2020–2050 by constructing a 3EAD-CGE (economy-energy-environmental-agricultural-dynamics Computable General Equilibrium) model. The results show that: (1) carbon tax has a time effect, specifically, the short-term effect is better than the long-term. (2) If the incremental rate of carbon tax is carried out alone, it will exert a great influence on the macroeconomy as well as on most of the agricultural related sectors. (3) If a carbon tax is introduced at the same time as indirect taxes are cut (proportionally), the policy will exert a negative impact on agriculture-related sectors that are subsidized. However, the policy will have a positive impact on those nonsubsidized sectors. Finally, based on the results, we put forward some suggestions that are more suitable for the introduction of a carbon tax in China’s agricultural-related sectors. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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16 pages, 760 KiB

Open AccessArticle

Quota Allocation for Carbon Emissions in China’s Electric Power Industry Based Upon the Fairness Principle

by Ming Meng, Lixue Wang and Qu Chen

Energies 2018, 11(9), 2256; https://doi.org/10.3390/en11092256 - 27 Aug 2018

Cited by 6 | Viewed by 3411

Abstract

As an essential measure to mitigate the CO₂ emissions, China is constructing a nationwide carbon emission trading (CET) market. The electric power industry is the first sector that will be introduced into this market, but the quota allocation scheme, as the key [...] Read more.

As an essential measure to mitigate the CO₂ emissions, China is constructing a nationwide carbon emission trading (CET) market. The electric power industry is the first sector that will be introduced into this market, but the quota allocation scheme, as the key foundation of market transactions, is still undetermined. This research employed the gross domestic product (GDP), energy consumption, and electric generation data of 30 provinces from 2001 to 2015, a hybrid trend forecasting model, and a three-indicator allocation model to measure the provincial quota allocation for carbon emissions in China’s electric power sector. The conclusions drawn from the empirical analysis can be summarized as follows: (1) The carbon emission peak in China’s electric power sector will appear in 2027, and peak emissions will be 3.63 billion tons, which will surpass the total carbon emissions of the European Union (EU) and approximately equal to 2/3 of the United States of America (USA). (2) The developed provinces that are supported by traditional industries should take more responsibility for carbon mitigation. (3) Nine provinces are expected to be the buyers in the CET market. These provinces are mostly located in eastern China, and account for approximately 63.65% of China’s carbon emissions generated by the electric power sector. (4) The long-distance electric power transmission shifts the carbon emissions and then has an impact on the quotas allocation for carbon emissions. (5) The development and effective utilization of clean power generation will play a positive role for carbon mitigation in China’s electric sector. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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22 pages, 1332 KiB

Open AccessFeature PaperArticle

Carbon Taxes and Carbon Right Costs Analysis for the Tire Industry

by Wen-Hsien Tsai

Energies 2018, 11(8), 2121; https://doi.org/10.3390/en11082121 - 14 Aug 2018

Cited by 11 | Viewed by 4554

Abstract

As enterprises are the major perpetrators of global climate change, concerns about global warming, climate change, and global greenhouse gas emissions continue to attract attention, and have become international concerns. The tire industry, which is a high-pollution, high-carbon emission industry, is facing pressure [...] Read more.

As enterprises are the major perpetrators of global climate change, concerns about global warming, climate change, and global greenhouse gas emissions continue to attract attention, and have become international concerns. The tire industry, which is a high-pollution, high-carbon emission industry, is facing pressure to reduce its carbon emissions. Thus, carbon prices and carbon trading have become issues of global importance. In order to solve this environmental problem, the purpose of this paper is to combine mathematical programming, Theory of Constraints (TOC), and Activity-Based Costing (ABC) to formulate the green production decision model with carbon taxes and carbon right costs, in order to achieve the optimal product mix decision under various constraints. This study proposes three different scenario models with carbon taxes and carbon right used to evaluate the effect on profit of changes in carbon tax rates. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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24 pages, 2488 KiB

Open AccessArticle

Green Production Planning and Control for the Textile Industry by Using Mathematical Programming and Industry 4.0 Techniques

by Wen-Hsien Tsai

Energies 2018, 11(8), 2072; https://doi.org/10.3390/en11082072 - 9 Aug 2018

Cited by 58 | Viewed by 11875

Abstract

The textile industry is one of the world’s major sources of industrial pollution, and related environmental issues are becoming an ever greater concern. This paper considers the environmental issues of carbon emissions, energy recycling, and waste reuse, and uses a mathematical programming model [...] Read more.

The textile industry is one of the world’s major sources of industrial pollution, and related environmental issues are becoming an ever greater concern. This paper considers the environmental issues of carbon emissions, energy recycling, and waste reuse, and uses a mathematical programming model with Activity-Based Costing (ABC) and the Theory of Constraints (TOC) to achieve profit maximization. This paper discusses the combination of mathematical programming and Industry 4.0 techniques to achieve the purpose of green production planning and control for the textile industry in the new era. The mathematical programming model is used to determine the optimal product mix under various production constraints, while Industry 4.0 techniques are used to control the production progress to achieve the planning targets. With the help of an Industry 4.0 real-time sensor and detection system, it can achieve the purposes of recycling waste, reducing carbon emission, saving energy and cost, and finally achieving a maximization of profit. The main contributions of this research are using mathematical programming approach to formulate the decision model with ABC cost data and TOC constraints for the textile companies and clarifying the relation between mathematical programming models and Industry 4.0 techniques. Managers in the textile companies can apply this decision model to achieve the optimal product-mix under various constraints and to evaluate the effect on profit of carbon emissions, energy recycling, waste reuse, and material quantity discount. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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18 pages, 4083 KiB

Open AccessArticle

The Efficiency of Long-Term Forecasting Model on Final Energy Consumption in Thailand’s Petroleum Industries Sector: Enriching the LT-ARIMAXS Model under a Sustainability Policy

by Pruethsan Sutthichaimethee and Kuskana Kubaha

Energies 2018, 11(8), 2063; https://doi.org/10.3390/en11082063 - 8 Aug 2018

Cited by 10 | Viewed by 2879

Abstract

Presently, Thailand runs various sustainable development-based policies to boost the growth in economy, society, and environment. In this study, the economic and social growth was found to continuously increase and negatively deteriorate the environment at the same time due to a more massive [...] Read more.

Presently, Thailand runs various sustainable development-based policies to boost the growth in economy, society, and environment. In this study, the economic and social growth was found to continuously increase and negatively deteriorate the environment at the same time due to a more massive final energy consumption in the petroleum industries sector than any other sectors. Therefore, it is necessary to establish national planning and it requires an effective forecasting model to support Thailand’s policy-making. This study aimed to construct a forecasting model for a final energy consumption prediction in Thailand’s petroleum industry sector for a longer-term (2018–2037) at a maximum efficiency from a certain class of methods. The Long Term-Autoregressive Integrated Moving Average with Exogeneous variables and Error Correction Mechanism model (LT-ARIMAXS model) (p, d, q, Xi, ECT_(t−1)) was adapted from the autoregressive and moving average model incorporating influential variables together in both long-term relationships to produce the best model for prediction performance. All relevant variables in the model are stationary at Level I(0) or Level I(1). In terms of the extraneous variables, they consist of per capita GDP, population growth, oil price, energy intensity, urbanization rate, industrial structure, and net exports. The study found that the variables used are the causal factors and stationary at the first difference as well as co-integrated. With such features, it reflects that the variables are influential over the final energy consumption. The LT-ARIMAXS model (2,1,2) determined a proper period (t − i) through a white noise process with the Q test statistical method. It shows that the LT-ARIMAXS model (2,1,2) does not generate the issues of heteroskedasticity, multicollinearity, and autocorrelation. The performance of LT-ARIMAXS model (2,1,2) was tested based on the mean absolute percentage error (MAPE) and the root mean square error (RMSE). The LT-ARIMAXS model (2,1,2) can predict the final energy consumption based on the Sustainable Development Plan for the 20 years from 2018 to 2037. The results showed that the final energy consumption continues to increase steadily by 121,461 ktoe in 2037. Furthermore, the findings present that the growth rate (2037/2017) increases by 109.8%, which is not in line with Thailand’s reduction policy. In this study, the MAPE was valued at 0.97% and RMSE was valued at 2.12% when compared to the other old models. Therefore, the LT-ARIMAXS model (2,1,2) can be useful and appropriate for policy-making to achieve sustainability. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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30 pages, 1902 KiB

Open AccessArticle

A Green Quality Management Decision Model with Carbon Tax and Capacity Expansion under Activity-Based Costing (ABC)—A Case Study in the Tire Manufacturing Industry

by Wen-Hsien Tsai

Energies 2018, 11(7), 1858; https://doi.org/10.3390/en11071858 - 16 Jul 2018

Cited by 7 | Viewed by 5564

Abstract

Issues related to global environmental protection are highly important. Under the global trend of energy saving and carbon reduction, in order to lower the carbon emissions of products or services offered by enterprises, the Taiwanese government aims to control carbon emissions by constructing [...] Read more.

Issues related to global environmental protection are highly important. Under the global trend of energy saving and carbon reduction, in order to lower the carbon emissions of products or services offered by enterprises, the Taiwanese government aims to control carbon emissions by constructing a carbon tax system and mandating enterprises to pay a carbon tax. The collection of a carbon tax can minimize the total social environmental cost and increase the efficiency of carbon reduction; the need to control the green quality cost can serve as a criterion of green management decision-making. This study aimed to reorganize carbon emissions in different stages of production in order to lower the total carbon emissions of products. Activity-based costing (ABC) was adopted to assess green quality management and production cost. The optimal green quality production portfolio was selected via a mathematical programming model to focus on the expansion of productivity and outsourcing strategy in order to effectively lessen the harmful effects on the environment and maximize profits. Besides academic contributions, the findings of this study could serve as a reference to enterprises on assessing the effects of carbon emissions, carbon taxes, and environmental management on production decision-making. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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10 pages, 989 KiB

Open AccessArticle

A Real Options Analysis for Renewable Energy Investment Decisions under China Carbon Trading Market

by Yanbin Li, Min Wu and Zhen Li

Energies 2018, 11(7), 1817; https://doi.org/10.3390/en11071817 - 11 Jul 2018

Cited by 20 | Viewed by 4061

Abstract

Under the carbon trading mechanism, renewable energy projects can gain additional benefits through Chinese Certified Emission Reduction transactions. Due to the uncertainty of carbon trading system, carbon prices will fluctuate randomly, which will affect the investment timing of renewable energy projects. Thus, the [...] Read more.

Under the carbon trading mechanism, renewable energy projects can gain additional benefits through Chinese Certified Emission Reduction transactions. Due to the uncertainty of carbon trading system, carbon prices will fluctuate randomly, which will affect the investment timing of renewable energy projects. Thus, the value of the option will be generated. Therefore, renewable energy power generation project investment has the right of option. However, the traditional investment decision-making method can no longer meet the requirements of renewable energy investment in the current stage. In this paper, a real option model considering carbon price fluctuation is proposed as a tool for renewable energy investment. Considering optimal investment timing and carbon price, the model introduces a carbon price fluctuation as part of the optimization, studies the flexibility of enterprises’ delayed investment under the fluctuation of carbon price. A case study is carried out to verify the effectiveness of the proposed real option model by selecting a wind farm in North China. The model is expected to help investors to assess the volatility and risk of renewable energy projects more accurately, and help investors to make a complete plan for the project investment, thus promoting the efficient allocation of resources in the energy industry. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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16 pages, 1436 KiB

Open AccessArticle

A Relational Analysis Model of the Causal Factors Influencing CO₂ in Thailand’s Industrial Sector under a Sustainability Policy Adapting the VARIMAX-ECM Model

by Pruethsan Sutthichaimethee and Kuskana Kubaha

Energies 2018, 11(7), 1704; https://doi.org/10.3390/en11071704 - 1 Jul 2018

Cited by 17 | Viewed by 2626

Abstract

Sustainable development is part and parcel of development policy for Thailand, in order to promote growth along with economic growth, social advancement, and environmental security. Thailand has, therefore, established a national target to reduce CO₂ emissions below 20.8%, or not exceeding 115 [...] Read more.

Sustainable development is part and parcel of development policy for Thailand, in order to promote growth along with economic growth, social advancement, and environmental security. Thailand has, therefore, established a national target to reduce CO₂ emissions below 20.8%, or not exceeding 115 Mt CO₂ Equivalent (Eq.) by 2029 within industries so as to achieve the country’s sustainable development target. Hence, it is necessary to have a certain measure to promote effective policies; in this case, a forecast of future CO₂ emissions in both the short and long run is used to optimize the forecasted result and to formulate correct and effective policies. The main purpose of this study is to develop a forecasting model, the so-called VARIMAX-ECM model, to forecast CO₂ emissions in Thailand, by deploying an analysis of the co-integration and error correction model. The VARIMAX-ECM model is adapted from the vector autoregressive model, incorporating influential variables in both short- and long-term relationships so as to produce the best model for better prediction performance. With this model, we attempt to fill the gaps of other existing models. In the model, only causal and influential factors are selected to establish the model. In addition, the factors must only be stationary at the first difference, while unnecessary variables will be discarded. This VARIMAX-ECM model fills the existing gap by deploying an analysis of a co-integration and error correction model in order to determine the efficiency of the model, and that creates an efficiency and effectiveness in prediction. This study finds that both short- and long-term causal factors affecting CO₂ emissions include per capita GDP, urbanization rate, industrial structure, and net exports. These variables can be employed to formulate the VARIMAX-ECM model through a performance test based on the mean absolute percentage error (MAPE) value. This illustrates that the VARIMAX-ECM model is one of the best models suitable for the future forecasting of CO₂ emissions. With the VARIMAX-ECM model employed to forecast CO₂ emissions for the period of 2018 to 2029, the results show that CO₂ emissions continue to increase steadily by 14.68%, or 289.58 Mt CO₂ Eq. by 2029, which is not in line with Thailand’s reduction policy. The MAPE is valued at 1.1% compared to the other old models. This finding indicates that the future sustainable development policy must devote attention to the real causal factors and ignore unnecessary factors that have no relationships to, or influences on, the policy. Thus, we can determine the right direction for better and effective development. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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13 pages, 2231 KiB

Open AccessArticle

Decoupling Greenhouse Gas Emissions from Crop Production: A Case Study in the Heilongjiang Land Reclamation Area, China

by Yu Zhang, Xiaojiao Zou, Caifen Xu and Qingshan Yang

Energies 2018, 11(6), 1480; https://doi.org/10.3390/en11061480 - 6 Jun 2018

Cited by 19 | Viewed by 3331

Abstract

Modern agriculture contributes significantly to greenhouse gas emissions in several ways. From the perspective of sustainability assessment, it is not enough to evaluate mitigation measures that rely only on emissions reductions. In this article, we use the method of decoupling analysis to construct [...] Read more.

Modern agriculture contributes significantly to greenhouse gas emissions in several ways. From the perspective of sustainability assessment, it is not enough to evaluate mitigation measures that rely only on emissions reductions. In this article, we use the method of decoupling analysis to construct a decoupling index based on carbon footprint and crop yield and evaluate the relationship between crop production and greenhouse gas emissions using the most modern grain production base in China as a case study. The results indicate that a weak but variable decoupling trend occurred from 2001 to 2015 and that each branch achieved on average a weak decoupling across the study period. In addition, rice production constituted 80% of the regional carbon footprint in a crop’s life cycle. The results of our analysis of rice production show that weak decoupling was the most common outcome but was not consistent because a weak coupling occurred in 2015. Each branch on average achieved a weak decoupling except for the SH branch. Our research indicates that high agricultural material inputs with low utilization efficiency contributed to the poor relationship between crop production and greenhouse gas emissions in the study area. Fertilizer, especially N fertilizer, was an important contributor to the total greenhouse gas emissions of crop production. As a supplement to carbon footprint assessment, this decoupling analysis helps local decision-makers diagnose the level of green growth, identify key options to mitigate greenhouse gas emissions from agriculture, and adopt more targeted interventions towards sustainable agriculture. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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17 pages, 1559 KiB

Open AccessArticle

Optimization of Inventory Routing Problem in Refined Oil Logistics with the Perspective of Carbon Tax

by Songyi Wang, Fengming Tao and Yuhe Shi

Energies 2018, 11(6), 1437; https://doi.org/10.3390/en11061437 - 4 Jun 2018

Cited by 25 | Viewed by 4143

Abstract

In order to solve the optimization problem of the refined oil distribution system from the perspectives of low-carbon and environmental protection, this paper focuses on the characteristics of the secondary distribution of refined oil and combines it with the integrated optimization concept of [...] Read more.

In order to solve the optimization problem of the refined oil distribution system from the perspectives of low-carbon and environmental protection, this paper focuses on the characteristics of the secondary distribution of refined oil and combines it with the integrated optimization concept of refined oil distribution network, where a low-carbon inventory routing problem (LCIRP) model is constructed with the minimum total costs as the objective function on the basis of considering carbon emissions. An adaptive genetic algorithm combined with greedy algorithm is designed to solve the model, and an example is given to verify the effectiveness of the algorithm. Then, this paper solves the model with two parts by introducing a practical numerical example: in the first part, the LCIRP models with different carbon tax values are solved, which verifies the effectiveness of the model and proves that carbon tax policies can effectively reduce the carbon emissions in the secondary distribution network of refined oil; in the second part, the LCIRP models with the different maximum load capacity of oil tank trucks are solved, which provides the economic and environmentally friendly distribution schemes for refined oil distribution enterprises under the premise of carbon tax policies and load limitation. Finally, the emission reduction proposals that take into account both economic and environmental benefits are given respectively from the aspect of government environmental protection agencies and from the aspect of refined oil distribution enterprises. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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17 pages, 3799 KiB

Open AccessArticle

Comparing Urban and Rural Household CO₂ Emissions—Case from China’s Four Megacities: Beijing, Tianjin, Shanghai, and Chongqing

by Rui Huang, Shaohui Zhang and Changxin Liu

Energies 2018, 11(5), 1257; https://doi.org/10.3390/en11051257 - 15 May 2018

Cited by 28 | Viewed by 5992

Abstract

CO₂ emissions caused by household consumption have become one of the main sources of greenhouse gas emissions. Studying household CO₂ emissions (HCEs) is of great significance to energy conservation and emissions reduction. In this study, we quantitatively analyzed the direct and [...] Read more.

CO₂ emissions caused by household consumption have become one of the main sources of greenhouse gas emissions. Studying household CO₂ emissions (HCEs) is of great significance to energy conservation and emissions reduction. In this study, we quantitatively analyzed the direct and indirect CO₂ emissions by urban and rural households in Beijing, Tianjin, Shanghai, and Chongqing. The results show that urban total HCEs are larger than rural total HCEs for the four megacities. Urban total per capita household CO₂ emissions (PHCEs) are larger than rural total PHCEs in Beijing, Tianjin, and Chongqing, while rural total PHCEs in Shanghai are larger than urban total PHCEs. Electricity and hot water production and supply was the largest contributor of indirect HCEs for both rural and urban households. Beijing, Tianjin, Shanghai, and Chongqing outsourced a large amount of indirect CO₂ emissions to their neighboring provinces. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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29 pages, 2123 KiB

Open AccessArticle

Influencing Factors and Decoupling Elasticity of China’s Transportation Carbon Emissions

by Yong Wang, Yu Zhou, Lin Zhu, Fei Zhang and Yingchun Zhang

Energies 2018, 11(5), 1157; https://doi.org/10.3390/en11051157 - 5 May 2018

Cited by 88 | Viewed by 7067

Abstract

Transportation is an important source of carbon emissions in China. Reduction in carbon emissions in the transportation sector plays a key role in the success of China’s energy conservation and emissions reduction. This paper, for the first time, analyzes the drivers of carbon [...] Read more.

Transportation is an important source of carbon emissions in China. Reduction in carbon emissions in the transportation sector plays a key role in the success of China’s energy conservation and emissions reduction. This paper, for the first time, analyzes the drivers of carbon emissions in China’s transportation sector from 2000 to 2015 using the Generalized Divisia Index Method (GDIM). Based on this analysis, we use the improved Tapio model to estimate the decoupling elasticity between the development of China’s transportation industry and carbon emissions. The results show that: (1) the added value of transportation, energy consumption and per capita carbon emissions in transportation have always been major contributors to China’s carbon emissions from transportation. Energy carbon emission intensity is a key factor in reducing carbon emissions in transportation. The carbon intensity of the added value and the energy intensity have a continuous effect on carbon emissions in transportation; (2) compared with the increasing factors, the decreasing factors have a limited effect on inhibiting the increase in carbon emissions in China’s transportation industry; (3) compared with the total carbon emissions decoupling state, the per capita decoupling state can more accurately reflect the relationship between transportation and carbon emissions in China. The state of decoupling between the development of the transportation industry and carbon emissions in China is relatively poor, with a worsening trend after a short period of improvement; (4) the decoupling of transportation and carbon emissions has made energy-saving elasticity more important than the per capita emissions reduction elasticity effect. Based on the conclusions of this study, this paper puts forward some policy suggestions for reducing carbon emissions in the transportation industry. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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25 pages, 2247 KiB

Open AccessArticle

The Scale, Structure and Influencing Factors of Total Carbon Emissions from Households in 30 Provinces of China—Based on the Extended STIRPAT Model

by Yong Wang, Guangchun Yang, Ying Dong, Yu Cheng and Peipei Shang

Energies 2018, 11(5), 1125; https://doi.org/10.3390/en11051125 - 2 May 2018

Cited by 48 | Viewed by 4550

Abstract

Household carbon emissions are important components of total carbon emissions. The consumer side of energy-saving emissions reduction is an essential factor in reducing carbon emissions. In this paper, the carbon emissions coefficient method and Consumer Lifestyle Approach (CLA) were used to calculate the [...] Read more.

Household carbon emissions are important components of total carbon emissions. The consumer side of energy-saving emissions reduction is an essential factor in reducing carbon emissions. In this paper, the carbon emissions coefficient method and Consumer Lifestyle Approach (CLA) were used to calculate the total carbon emissions of households in 30 provinces of China from 2006 to 2015, and based on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the factors influencing the total carbon emissions of households were analyzed. The results indicated that, first, over the past ten years, the energy and products carbon emissions from China’s households have demonstrated a rapid growth trend and that regional distributions present obvious differences. Second, China’s energy carbon emissions due to household consumption primarily derived from the residents’ consumption of electricity and coal; China’s products household carbon emissions primarily derived from residents’ consumption of the high carbon emission categories: residences, food, transportation and communications. Third, in terms of influencing factors, the number of households in China plays a significant role in the total carbon emissions of China’s households. The ratio of children 0–14 years old and gender ratio (female = 100) are two factors that reflect the demographic structure, have significant effects on the total carbon emissions of China’s households, and are all positive. Gross Domestic Product (GDP) per capita plays a role in boosting the total carbon emissions of China’s households. The effect of the carbon emission intensity on total household carbon emissions is positive. The industrial structure (the proportion of secondary industries’ added value to the regional GDP) has curbed the growth of total carbon emissions from China’s household consumption. The results of this study provide data to support the assessment of the total carbon emissions of China’s households and provide a reasonable reference that the government can use to formulate energy-saving and emission-reduction measures. Full article

(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)

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