Pub Date : 2024-01-14DOI: 10.13052/spee1048-5236.43210
Yisheng Liu, Fang Xu
Due to the increasing demand for fossil fuels, excessive emissions of greenhouse gases such as CO2 have been caused. With the intensification of global climate anomalies and warming, how to reduce greenhouse gas emissions is an important issue currently facing the international community. The influencing factors of carbon price are complex, and accurate prediction of carbon price is a difficult problem. There are still some problems in the existing carbon trading price prediction models, such as insufficient understanding of the enormous potential of machine learning models to ilift the performance. The study will use two machine learning models that can address the shortcomings of traditional artificial intelligence models as the basic prediction models. The specific content includes machine learning prediction models that extend to extreme learning machine theory and fuzzy inference system theory. By integrating data preprocessing algorithms, artificial intelligence optimization algorithms, feature selection algorithms, etc., this study constructs and applies a carbon trading price prediction model from multiple perspectives to compensate for the shortcomings in current research. The corresponding values for each indicator in the algorithm are 5.6214E-12 (maximum), 2.8546E-12 (minimum), 4.0239E-12 (mean), and 5.4402E-13 (variance). Compared with other comparative optimization algorithms, this indicates that the hybrid optimization algorithm is an efficient optimization method for the model, which can effectively optimize different problems. In theory, the proposed multiple improved carbon trading price prediction models can theoretically compensate for the shortcomings in existing carbon trading price predictions.
{"title":"Application of Digital Economy Machine Learning Algorithm for Predicting Carbon Trading Prices Under Carbon Reduction Trends","authors":"Yisheng Liu, Fang Xu","doi":"10.13052/spee1048-5236.43210","DOIUrl":"https://doi.org/10.13052/spee1048-5236.43210","url":null,"abstract":"Due to the increasing demand for fossil fuels, excessive emissions of greenhouse gases such as CO2 have been caused. With the intensification of global climate anomalies and warming, how to reduce greenhouse gas emissions is an important issue currently facing the international community. The influencing factors of carbon price are complex, and accurate prediction of carbon price is a difficult problem. There are still some problems in the existing carbon trading price prediction models, such as insufficient understanding of the enormous potential of machine learning models to ilift the performance. The study will use two machine learning models that can address the shortcomings of traditional artificial intelligence models as the basic prediction models. The specific content includes machine learning prediction models that extend to extreme learning machine theory and fuzzy inference system theory. By integrating data preprocessing algorithms, artificial intelligence optimization algorithms, feature selection algorithms, etc., this study constructs and applies a carbon trading price prediction model from multiple perspectives to compensate for the shortcomings in current research. The corresponding values for each indicator in the algorithm are 5.6214E-12 (maximum), 2.8546E-12 (minimum), 4.0239E-12 (mean), and 5.4402E-13 (variance). Compared with other comparative optimization algorithms, this indicates that the hybrid optimization algorithm is an efficient optimization method for the model, which can effectively optimize different problems. In theory, the proposed multiple improved carbon trading price prediction models can theoretically compensate for the shortcomings in existing carbon trading price predictions.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":"94 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139530122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-14DOI: 10.13052/spee1048-5236.4326
Jingling Qin
Energy shortages and environmental degradation are issues that are getting more and more significant globally. New energy vehicles are being promoted by the state due to the advantages of low pollution and low fuel consumption. However, due to battery technology, the range of new energy vehicles cannot meet the needs of users. As the most energy-efficient auxiliary device, the energy consumption of air conditioning will significantly reduce the range of new energy vehicles. In low temperature environments, heating energy consumption will reduce the range of vehicles by more than 50%. Therefore, the research aims to reduce the energy consumption of air conditioning systems in new energy vehicles by reducing load demand and improving operating efficiency. The study designs a low-temperature heat pump air conditioning system based on digital technology and then uses a computational algorithm to construct an energy consumption model for the heat pump air conditioning system of a new energy vehicle. According to the test results, the system’s average increase in heat production after activating the enthalpy charge is 35% and its average COP is 0.14% lower than when switched off. At -5oC, the air outlet temperature of the system reaches up to 50.0oC. Summer cooling energy consumption increases exponentially with temperature, while winter heating energy consumption decreases linearly with temperature. In addition, the range decreases significantly as the ambient temperature deviates from the human comfort zone. The decline in the winter range is more severe than that in summer, moreover, the range of modern energy vehicles is reduced by 30% at an average winter temperature of -10oC. In summary, the low-temperature heat pump system offers greater performance. It is more useful in real-world applications and can offer a rational alternative to air conditioning’s energy-saving tactics.
{"title":"Research on Energy Consumption Model of Heat Pump Air Conditioning System for New Energy Vehicles Based on Digital Technology","authors":"Jingling Qin","doi":"10.13052/spee1048-5236.4326","DOIUrl":"https://doi.org/10.13052/spee1048-5236.4326","url":null,"abstract":"Energy shortages and environmental degradation are issues that are getting more and more significant globally. New energy vehicles are being promoted by the state due to the advantages of low pollution and low fuel consumption. However, due to battery technology, the range of new energy vehicles cannot meet the needs of users. As the most energy-efficient auxiliary device, the energy consumption of air conditioning will significantly reduce the range of new energy vehicles. In low temperature environments, heating energy consumption will reduce the range of vehicles by more than 50%. Therefore, the research aims to reduce the energy consumption of air conditioning systems in new energy vehicles by reducing load demand and improving operating efficiency. The study designs a low-temperature heat pump air conditioning system based on digital technology and then uses a computational algorithm to construct an energy consumption model for the heat pump air conditioning system of a new energy vehicle. According to the test results, the system’s average increase in heat production after activating the enthalpy charge is 35% and its average COP is 0.14% lower than when switched off. At -5oC, the air outlet temperature of the system reaches up to 50.0oC. Summer cooling energy consumption increases exponentially with temperature, while winter heating energy consumption decreases linearly with temperature. In addition, the range decreases significantly as the ambient temperature deviates from the human comfort zone. The decline in the winter range is more severe than that in summer, moreover, the range of modern energy vehicles is reduced by 30% at an average winter temperature of -10oC. In summary, the low-temperature heat pump system offers greater performance. It is more useful in real-world applications and can offer a rational alternative to air conditioning’s energy-saving tactics.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139623230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-24DOI: 10.13052/spee1048-5236.4313
Yin Jie
Objective and effective prediction of energy consumption can not only optimize the energy consumption structure, but also provide important information for the government to formulate energy conservation and emission reduction measures. With the development of new energy sources and changes in the global energy consumption structure, historical energy data that are too old may no longer be reliable for forecasting, which leads to a decrease in the amount of information on energy, and the gray theory, which is applicable to “poor information”, has gained attention. Firstly, the optimization of energy economic objectives and transformation path methods at this stage is clarified; then, the DEA-Malmqusit model is used to improve the shortcomings of the traditional model that can only compare different cross-sections at the same time node, and to evaluate and analyze the full-factor multi-indicators of energy enterprises in terms of technological empowerment, environmental dynamics, and economic output efficiency; finally, the LEAPS-based energy system consumption and load capacity prediction model. The results show that the traditional algorithm is not accurate enough and has some deviation when the energy raw data fluctuates a lot. The algorithm proposed in this paper still gives a better prediction, predicting a city’s carbon emission to be 65,240,100 tons in 2024, with a 3.6% increase in energy output year by year.
{"title":"Application of Gray Expansion Model in Energy Economic Analysis and Load Forecasting","authors":"Yin Jie","doi":"10.13052/spee1048-5236.4313","DOIUrl":"https://doi.org/10.13052/spee1048-5236.4313","url":null,"abstract":"Objective and effective prediction of energy consumption can not only optimize the energy consumption structure, but also provide important information for the government to formulate energy conservation and emission reduction measures. With the development of new energy sources and changes in the global energy consumption structure, historical energy data that are too old may no longer be reliable for forecasting, which leads to a decrease in the amount of information on energy, and the gray theory, which is applicable to “poor information”, has gained attention. Firstly, the optimization of energy economic objectives and transformation path methods at this stage is clarified; then, the DEA-Malmqusit model is used to improve the shortcomings of the traditional model that can only compare different cross-sections at the same time node, and to evaluate and analyze the full-factor multi-indicators of energy enterprises in terms of technological empowerment, environmental dynamics, and economic output efficiency; finally, the LEAPS-based energy system consumption and load capacity prediction model. The results show that the traditional algorithm is not accurate enough and has some deviation when the energy raw data fluctuates a lot. The algorithm proposed in this paper still gives a better prediction, predicting a city’s carbon emission to be 65,240,100 tons in 2024, with a 3.6% increase in energy output year by year.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":"332 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139160852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-24DOI: 10.13052/spee1048-5236.4317
Maamar Traich, Amal Rahmane
Between 2000 and 2020, the residential sector’s share of Algeria’s final energy consumption was 62.20% and 46.05% respectively; this was much higher than the global average of 31%. This enables us to comprehend the factors that contribute to the rise in energy consumption in the residential sector and to carry out a quantitative analysis to determine the extent to which energy efficiency reduces energy consumption in the sector between 2000 and 2020. To accomplish this, we employ the decomposition analysis approach by using the Logarithmic Mean Divisia Index (LMDI). The results of the study showed that the energy subsidy factor had a strong impact on increasing residential energy consumption in Algeria, compared to the population growth factor. In addition, the energy intensity factor and the economic structure factor did not help reduce energy consumption levels. According to these findings, Algerian public policymakers should strictly implement the national energy program Horizon 2030 by rationalising energy use, eliminating subsidies gradually, and monitoring the implementation of energy efficiency measures in the residential sector.
{"title":"A Decomposition Analysis of Algeria’s Residential Energy Consumption Change: (2000–2020)","authors":"Maamar Traich, Amal Rahmane","doi":"10.13052/spee1048-5236.4317","DOIUrl":"https://doi.org/10.13052/spee1048-5236.4317","url":null,"abstract":"Between 2000 and 2020, the residential sector’s share of Algeria’s final energy consumption was 62.20% and 46.05% respectively; this was much higher than the global average of 31%. This enables us to comprehend the factors that contribute to the rise in energy consumption in the residential sector and to carry out a quantitative analysis to determine the extent to which energy efficiency reduces energy consumption in the sector between 2000 and 2020. To accomplish this, we employ the decomposition analysis approach by using the Logarithmic Mean Divisia Index (LMDI). The results of the study showed that the energy subsidy factor had a strong impact on increasing residential energy consumption in Algeria, compared to the population growth factor. In addition, the energy intensity factor and the economic structure factor did not help reduce energy consumption levels. According to these findings, Algerian public policymakers should strictly implement the national energy program Horizon 2030 by rationalising energy use, eliminating subsidies gradually, and monitoring the implementation of energy efficiency measures in the residential sector.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":"1987 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139160534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-24DOI: 10.13052/spee1048-5236.4312
Wang Yang, Gao Yi, Zou Zhiyu, Chen Yue, Xudong Wang, Luo Shuai, Liu Ning, Zhou Jin, Yan Dawei
Excessive carbon emissions will lead to catastrophic consequences such as global warming and rising oceans and will also have a serious negative impact on the human food supply and living environment. The steel industry is characterized by high pollution, and about 18% of China’s carbon emissions come from the steel industry. The ‘double carbon’ strategy has brought important tasks and severe challenges to China’s steel industry. With a view to evaluating the achievements of carbon emission control, carbon emission monitoring systems at home and abroad have been continuously established and improved. For the steel industry, accurate and efficient carbon monitoring technology has a guiding role in guiding energy conservation and carbon reduction. Traditional carbon emission accounting methods have some problems, such as long cycles and poor data quality, which restrict the improvement of the lean level of carbon emission monitoring management. Firstly, this paper investigates and analyzes the productive process and carbon emission process of the steel industry and constructs an entropy weight-grey correlation -TOPSIS analysis method for the correlation between carbon emissions and influencing factors. Based on the above content, a carbon emission monitoring method based on multiple influencing factors is put forward, and the high monitoring accuracy of the model is proved by taking the Tianjin steel industry as an example. The results show that information mining of relevant data can strikingly increase the accuracy of carbon emission monitoring in the steel industry.
{"title":"Correlation Analysis and Monitoring Method of Carbon Emissions in the Steel Industry Based on Big Data","authors":"Wang Yang, Gao Yi, Zou Zhiyu, Chen Yue, Xudong Wang, Luo Shuai, Liu Ning, Zhou Jin, Yan Dawei","doi":"10.13052/spee1048-5236.4312","DOIUrl":"https://doi.org/10.13052/spee1048-5236.4312","url":null,"abstract":"Excessive carbon emissions will lead to catastrophic consequences such as global warming and rising oceans and will also have a serious negative impact on the human food supply and living environment. The steel industry is characterized by high pollution, and about 18% of China’s carbon emissions come from the steel industry. The ‘double carbon’ strategy has brought important tasks and severe challenges to China’s steel industry. With a view to evaluating the achievements of carbon emission control, carbon emission monitoring systems at home and abroad have been continuously established and improved. For the steel industry, accurate and efficient carbon monitoring technology has a guiding role in guiding energy conservation and carbon reduction. Traditional carbon emission accounting methods have some problems, such as long cycles and poor data quality, which restrict the improvement of the lean level of carbon emission monitoring management. Firstly, this paper investigates and analyzes the productive process and carbon emission process of the steel industry and constructs an entropy weight-grey correlation -TOPSIS analysis method for the correlation between carbon emissions and influencing factors. Based on the above content, a carbon emission monitoring method based on multiple influencing factors is put forward, and the high monitoring accuracy of the model is proved by taking the Tianjin steel industry as an example. The results show that information mining of relevant data can strikingly increase the accuracy of carbon emission monitoring in the steel industry.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":"2003 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139160207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-24DOI: 10.13052/spee1048-5236.4311
Naiyer Mumtaz, Md Irfan Ahmed, F. I. Bakhsh
In India, the adoption of sustainable and efficient renewable energy systems has become imperative for achieving sustainable development. Nowadays in India about 60% of the population has access to grid power, but due to the unreliable nature of electricity users, it is still necessary to rely on biofuels such as solar and animal waste for everyday activities like heating and cooking. With over 370 million tons of biomass produced annually in India, there is a significant market opportunity for biomass boilers in the country. This research paper proposes a hybrid system comprising of solar, and biomass from an end-user perspective. The proposed hybrid system has been modelled and analyzed using multisim software. Furthermore, the study assesses the feasibility of the proposed low-cost hybrid power system blueprint for the outlying regions of Baheya village, Ranchi, India, by utilizing the Multi-Criteria Decision Making (MCDM) technique. It has been observed that the total per unit cost of a hybrid system, which is Rs. 1.76, is lower compared to the individual per unit costs of solar and biomass plants, which are Rs. 1.628 and Rs. 0.433, respectively. The analysis shows that the proposed hybrid system is a reliable solution for providing electricity in the Baheya village.
{"title":"Assessment of Solar-Biomass Using MCDM Technique: Case Study of Ranchi, India","authors":"Naiyer Mumtaz, Md Irfan Ahmed, F. I. Bakhsh","doi":"10.13052/spee1048-5236.4311","DOIUrl":"https://doi.org/10.13052/spee1048-5236.4311","url":null,"abstract":"In India, the adoption of sustainable and efficient renewable energy systems has become imperative for achieving sustainable development. Nowadays in India about 60% of the population has access to grid power, but due to the unreliable nature of electricity users, it is still necessary to rely on biofuels such as solar and animal waste for everyday activities like heating and cooking. With over 370 million tons of biomass produced annually in India, there is a significant market opportunity for biomass boilers in the country. This research paper proposes a hybrid system comprising of solar, and biomass from an end-user perspective. The proposed hybrid system has been modelled and analyzed using multisim software. Furthermore, the study assesses the feasibility of the proposed low-cost hybrid power system blueprint for the outlying regions of Baheya village, Ranchi, India, by utilizing the Multi-Criteria Decision Making (MCDM) technique. It has been observed that the total per unit cost of a hybrid system, which is Rs. 1.76, is lower compared to the individual per unit costs of solar and biomass plants, which are Rs. 1.628 and Rs. 0.433, respectively. The analysis shows that the proposed hybrid system is a reliable solution for providing electricity in the Baheya village.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":"2016 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139159806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-24DOI: 10.13052/spee1048-5236.4316
Huifang Guo, Jian Meng, Hairong Huang, Shixia Zhang, Denghong Wang
In 2021, Wenzhou adopted measures to restrict the use of electricity, and the shortage of electricity became an important factor affecting the production and life of Wenzhou. Nanxi River is one of the main rivers in Wenzhou City, and its water resources are very rich. According to the statistics of the water conservancy planning of the Nanxi River basin, there are 96 hydropower stations in the Nanxi River basin, with a total installed capacity of 152100 kW, accounting for 57% of the installed capacity. The development and utilization of the Nanxi River water resources can alleviate the power shortage in Wenzhou power grid to a certain extent. The development and utilization of hydropower are closely related to the runoff of the basin. The river runoff is mainly determined by rainfall, underlying surface and upstream inflow. River runoff is affected by many factors in the process of formation, so it is difficult to improve its prediction accuracy. In order to improve the prediction accuracy of the runoff of the main stream of the Nanxi River, this paper introduces the runoff prediction model of particle swarm optimization adaptive fuzzy inference system (PSO-ANFIS). ANFIS model has the advantages of applying fuzzy rules and the nonlinear approximation ability of neural network, but the antecedent parameters of ANFIS model are prone to fall into local optimization. In order to improve the generalization ability of the antecedent parameters of ANFIS model, the PSO algorithm of global optimization is introduced to optimize the antecedent parameters of ANFIS. Through the application of the example, it is found that the decision coefficient of PSO-ANFIS model in the simulation stage is 0.987, and the decision coefficient in the prediction stage is 0.856. This model can be applied in the annual runoff forecast. Through comparison with ANFIS model, it is found that PSO-ANFIS model has better prediction effect.
{"title":"Study on the Annual Runoff Forecast Model of the Main Stream of Nanxi River Based on PSO-ANFIS","authors":"Huifang Guo, Jian Meng, Hairong Huang, Shixia Zhang, Denghong Wang","doi":"10.13052/spee1048-5236.4316","DOIUrl":"https://doi.org/10.13052/spee1048-5236.4316","url":null,"abstract":"In 2021, Wenzhou adopted measures to restrict the use of electricity, and the shortage of electricity became an important factor affecting the production and life of Wenzhou. Nanxi River is one of the main rivers in Wenzhou City, and its water resources are very rich. According to the statistics of the water conservancy planning of the Nanxi River basin, there are 96 hydropower stations in the Nanxi River basin, with a total installed capacity of 152100 kW, accounting for 57% of the installed capacity. The development and utilization of the Nanxi River water resources can alleviate the power shortage in Wenzhou power grid to a certain extent. The development and utilization of hydropower are closely related to the runoff of the basin. The river runoff is mainly determined by rainfall, underlying surface and upstream inflow. River runoff is affected by many factors in the process of formation, so it is difficult to improve its prediction accuracy. In order to improve the prediction accuracy of the runoff of the main stream of the Nanxi River, this paper introduces the runoff prediction model of particle swarm optimization adaptive fuzzy inference system (PSO-ANFIS). ANFIS model has the advantages of applying fuzzy rules and the nonlinear approximation ability of neural network, but the antecedent parameters of ANFIS model are prone to fall into local optimization. In order to improve the generalization ability of the antecedent parameters of ANFIS model, the PSO algorithm of global optimization is introduced to optimize the antecedent parameters of ANFIS. Through the application of the example, it is found that the decision coefficient of PSO-ANFIS model in the simulation stage is 0.987, and the decision coefficient in the prediction stage is 0.856. This model can be applied in the annual runoff forecast. Through comparison with ANFIS model, it is found that PSO-ANFIS model has better prediction effect.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":"1978 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139160479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-24DOI: 10.13052/spee1048-5236.4314
Kunyue Zhang, Mingru Tao, Jinmin Hao
Climate change is one of the major challenges currently facing the world. The factors influencing the carbon emission of energy consumption and the future trend are important guidance for proposing scientific carbon reduction strategies to mitigate climate change. In this paper, the Logarithmic Mean Divisia Index (LMDI) model and stochastic impacts by regression population, affluence and technology (STIRPAT) model are established to analyze and predict the carbon emission of energy consumption. The LMDI model is used to factorize the CO2 changes generated by residential domestic energy consumption, and to decompose and analyze the carbon emission factors of residential domestic energy consumption in terms of energy carbon emission intensity, energy consumption structure, energy consumption intensity, economic development, and population to determine the driving factors leading to carbon emission changes; based on the above study, we set up nine different development scenarios and applied the scalable stochastic environmental impact assessment model to project energy carbon emissions in 2035; based on carbon emission prediction and analysis, the CO2 emissions of total energy consumption, total electricity consumption, industrial energy consumption and terminal energy consumption were selected, and the correlation coefficients with relevant climate indicators such as temperature change and humidity change were analyzed, and the stress model of energy consumption on climate change was constructed. The results show that: the correlation coefficients of energy consumption indicators and temperature change indicators all pass the significance test at P = 0.01 level, among which the correlation coefficients with temperature difference are the highest, all of them are greater than 0.9 and pass the significance test at P = 0.001 level; among the indicators of energy consumption, the correlation coefficient between total industrial energy consumption and temperature difference was slightly higher than that of total energy consumption and electricity consumption; the stress relationship between the increase of energy consumption and the temperature difference is consistent with the growth of the third polynomial curve.
气候变化是当前世界面临的重大挑战之一。能源消耗碳排放的影响因素及未来趋势对提出科学的碳减排策略以减缓气候变化具有重要指导意义。本文建立了对数平均指数（LMDI）模型和回归人口、富裕程度和技术的随机影响（STIRPAT）模型来分析和预测能源消耗的碳排放。利用 LMDI 模型对居民生活能源消费产生的二氧化碳变化进行因子化，从能源碳排放强度、能源消费结构、能源消费强度、经济发展、人口等方面对居民生活能源消费的碳排放因子进行分解分析，确定导致碳排放变化的驱动因素；在上述研究的基础上，设定九种不同的发展情景，应用可扩展的随机环境影响评估模型对 2035 年的能源碳排放进行预测；在碳排放预测分析的基础上，选取能源消费总量、电力消费总量、工业能源消费总量和终端能源消费总量的二氧化碳排放量，分析其与气温变化、湿度变化等相关气候指标的相关系数，构建能源消费对气候变化的压力模型。结果表明：能耗指标与温度变化指标的相关系数均在 P = 0.01 水平上通过显著性检验，其中与温差的相关系数最高，均大于 0.9，且在 P = 0.001 水平下通过显著性检验；在能耗指标中，工业总能耗与温差的相关系数略高于总能耗与电耗的相关系数；能耗增加与温差的应力关系符合三次多项式曲线的增长关系。
{"title":"Analysis and Prediction of Factors Influencing Carbon Emissions of Energy Consumption Under Climate Change","authors":"Kunyue Zhang, Mingru Tao, Jinmin Hao","doi":"10.13052/spee1048-5236.4314","DOIUrl":"https://doi.org/10.13052/spee1048-5236.4314","url":null,"abstract":"Climate change is one of the major challenges currently facing the world. The factors influencing the carbon emission of energy consumption and the future trend are important guidance for proposing scientific carbon reduction strategies to mitigate climate change. In this paper, the Logarithmic Mean Divisia Index (LMDI) model and stochastic impacts by regression population, affluence and technology (STIRPAT) model are established to analyze and predict the carbon emission of energy consumption. The LMDI model is used to factorize the CO2 changes generated by residential domestic energy consumption, and to decompose and analyze the carbon emission factors of residential domestic energy consumption in terms of energy carbon emission intensity, energy consumption structure, energy consumption intensity, economic development, and population to determine the driving factors leading to carbon emission changes; based on the above study, we set up nine different development scenarios and applied the scalable stochastic environmental impact assessment model to project energy carbon emissions in 2035; based on carbon emission prediction and analysis, the CO2 emissions of total energy consumption, total electricity consumption, industrial energy consumption and terminal energy consumption were selected, and the correlation coefficients with relevant climate indicators such as temperature change and humidity change were analyzed, and the stress model of energy consumption on climate change was constructed. The results show that: the correlation coefficients of energy consumption indicators and temperature change indicators all pass the significance test at P = 0.01 level, among which the correlation coefficients with temperature difference are the highest, all of them are greater than 0.9 and pass the significance test at P = 0.001 level; among the indicators of energy consumption, the correlation coefficient between total industrial energy consumption and temperature difference was slightly higher than that of total energy consumption and electricity consumption; the stress relationship between the increase of energy consumption and the temperature difference is consistent with the growth of the third polynomial curve.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":"570 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139160905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to investigate the effect of different substrates of constructed wetlands on the purification of polluted water in rivers and their greenhouse gas emissions, this study designed three small-scale constructed wetland experimental systems with traditional gravel (CW-G), volcanic rock (CW-V) and biomass carbon (CW-B) as filler substrates to investigate the effect of different constructed wetland systems on the removal of COD and nitrogen pollutants and to further analyse their effect on greenhouse gas emissions. The results showed that the removal rates of organic matter in all three groups of constructed wetlands reached over 90%. and 49.29% to 58.71%, respectively, with CW-V and CW-B significantly improving the removal of NH4 + -N and NO3− -N compared to CW-G (P < 0.05). A comparison of greenhouse gas emissions reveals that although CW-B resulted in the highest N2O emissions due to its better removal of NO3− -N, its share in nitrogen removal was still the smallest. In addition, the rapid consumption of organic matter in the influent water and the oxidation of some CH4 to CO2 resulted in no detectable CH4 in any of the three groups of constructed wetlands. The results of this study show that the differences in treatment effects and greenhouse gas emissions between the three types of substrate constructed wetlands are significant, and this study can provide some scientific reference for the construction and operation of wetlands for the purification of polluted water bodies in rivers.
{"title":"Study on the Effectiveness of Constructed Wetlands in Purifying Polluted Water from Rivers and Greenhouse Gas Emissions","authors":"Likang Zhu, Zhiping Sun, Shixia Zhang, Chenglong Ma, Denghong Wang, Qiankun Hong","doi":"10.13052/spee1048-5236.4315","DOIUrl":"https://doi.org/10.13052/spee1048-5236.4315","url":null,"abstract":"In order to investigate the effect of different substrates of constructed wetlands on the purification of polluted water in rivers and their greenhouse gas emissions, this study designed three small-scale constructed wetland experimental systems with traditional gravel (CW-G), volcanic rock (CW-V) and biomass carbon (CW-B) as filler substrates to investigate the effect of different constructed wetland systems on the removal of COD and nitrogen pollutants and to further analyse their effect on greenhouse gas emissions. The results showed that the removal rates of organic matter in all three groups of constructed wetlands reached over 90%. and 49.29% to 58.71%, respectively, with CW-V and CW-B significantly improving the removal of NH4 + -N and NO3− -N compared to CW-G (P < 0.05). A comparison of greenhouse gas emissions reveals that although CW-B resulted in the highest N2O emissions due to its better removal of NO3− -N, its share in nitrogen removal was still the smallest. In addition, the rapid consumption of organic matter in the influent water and the oxidation of some CH4 to CO2 resulted in no detectable CH4 in any of the three groups of constructed wetlands. The results of this study show that the differences in treatment effects and greenhouse gas emissions between the three types of substrate constructed wetlands are significant, and this study can provide some scientific reference for the construction and operation of wetlands for the purification of polluted water bodies in rivers.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":"1980 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139160429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The current Chinese electricity market and carbon market are built relatively independently, without coupling and synergy, and the incoherence between the two markets is beginning to emerge. Carbon emissions costs in the carbon trading market will affect the marginal cost of renewable energy and thermal power in the electricity market, limiting market participants’ profitability. In order to simulate and evaluate the changes of LCOE of renewable energy and thermal power in the “electricity-carbon” market environment, this paper presents the calculation method of carbon emission cost of thermal power and CCER benefit of renewable energy based on the relevant regulations in China, and calculates the carbon emission cost transmission rate of thermal power based on Cournot model. In addition, we proposed a method for calculating the LCOE based on the international common calculation method for LCOE, combined with China’s taxation policy and the cost and benefit factors of renewable energy and thermal power in the carbon market, and proposed a method for calculating the LCOE applicable to the “electricity-carbon” market environment in China. The findings indicate that as a result of the influence of the carbon market, the levelized cost of energy (LCOE) cost of thermal power will increase, and the profitability of thermal power in the electricity market will be further reduced. On the other hand, the LCOE cost of renewable energy will further decrease, and its profitability will improve due to the additional CCER benefits in the carbon market.
{"title":"LCOE Calculation Method Based on Carbon Cost Transmission in an “Electricity-Carbon” Market Environment","authors":"Jian Zhang, Qian Sun, Xiaohe Liang, Jian Chen, Jipeng Kuai, Nannan Xia","doi":"10.13052/spee1048-5236.4244","DOIUrl":"https://doi.org/10.13052/spee1048-5236.4244","url":null,"abstract":"The current Chinese electricity market and carbon market are built relatively independently, without coupling and synergy, and the incoherence between the two markets is beginning to emerge. Carbon emissions costs in the carbon trading market will affect the marginal cost of renewable energy and thermal power in the electricity market, limiting market participants’ profitability. In order to simulate and evaluate the changes of LCOE of renewable energy and thermal power in the “electricity-carbon” market environment, this paper presents the calculation method of carbon emission cost of thermal power and CCER benefit of renewable energy based on the relevant regulations in China, and calculates the carbon emission cost transmission rate of thermal power based on Cournot model. In addition, we proposed a method for calculating the LCOE based on the international common calculation method for LCOE, combined with China’s taxation policy and the cost and benefit factors of renewable energy and thermal power in the carbon market, and proposed a method for calculating the LCOE applicable to the “electricity-carbon” market environment in China. The findings indicate that as a result of the influence of the carbon market, the levelized cost of energy (LCOE) cost of thermal power will increase, and the profitability of thermal power in the electricity market will be further reduced. On the other hand, the LCOE cost of renewable energy will further decrease, and its profitability will improve due to the additional CCER benefits in the carbon market.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42899854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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