An Agent-Based Model to Project China’s Energy Consumption and Carbon Emission Peaks at Multiple Levels

Wu, Jing; Mohamed, Rayman; Wang, Zheng

doi:10.3390/su9060893

Cited by 19 publications

(13 citation statements)

References 27 publications

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“…Assume thatα,β,γ are the parametric estimated values of Equation (14), replacing t e (t + 1) =αt (1) e (t) +βt +γ, to minimise the simulation error, the following condition needs to be satisfied:…”

Section: Homologous Grey Energy Prediction Modelmentioning

confidence: 99%

“…In Section 2, we build a new grey prediction model, HGEM (1,1). In Section 3, we study the error-checking method for the HGEM(1,1) model.…”

Section: Introductionmentioning

confidence: 99%

“…Since the 1990s, China's manufacturing industry has continued to develop at a high speed and has become the main driving force of the continued rapid development of China's economy [1][2][3]. The average annual growth rate of China's manufacturing industry was 15.8% and the total value of the output of China's manufacturing industry increased by 407.4% during 1995-2006 [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Forecasting the Energy Consumption of China’s Manufacturing Using a Homologous Grey Prediction Model

2017

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Abstract:With the rapid development of China's manufacturing, energy consumption has increased rapidly, and this has become a major bottleneck affecting the sustainable development of China's economy. This paper deduces and constructs a homologous grey prediction model with one variable and one first order equation (HGEM(1,1)) for forecasting the total energy consumption of China's manufacturing based on the Grey system theory. Both parameter estimation (PE) and the deduction of the final restored expression (FRE) of the HGEM(1,1) model are all from the time response expression of the whitenization differential equation, which solves the 'non-homologous' defects of PE and FRE with traditional grey prediction models. HGEM(1,1) has good performance and can unbiasedly simulate a homogeneous/non-homogeneous exponential function sequence and a linear function sequence. Then, the HGEM(1,1)model is used to simulate and forecast the total energy consumption of China's energy manufacturing, and the results show that the comprehensive performance of this model is much better than that of the classic Grey Model with one variable and single order equation, GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, we forecast the total energy consumption of China's manufacturing industry during the years 2018-2024. The results show that the total energy consumption in China's manufacturing is slowing down but is still too large. For this, some measures, such as optimizing the manufacturing structure and speeding up the development and promotion of energy saving and emission reduction technologies, to ensure the effective supply of energy in China's manufacturing industry are suggested.

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Section: Homologous Grey Energy Prediction Modelmentioning

confidence: 99%

“…In Section 2, we build a new grey prediction model, HGEM (1,1). In Section 3, we study the error-checking method for the HGEM(1,1) model.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecasting the Energy Consumption of China’s Manufacturing Using a Homologous Grey Prediction Model

2017

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“…How to realize this promise without compromising the socio-economic development has become an important issue that needs to be solved urgently. Therefore, empirical analysis and projection on carbon emissions are useful for the government to provide a theoretical and scientific basis for the sustainable development of low-carbon economy [4].At present, the research on energy-related carbon emissions are mainly classified as the calculation of carbon emissions [7-9], analysis of decomposition methods and influencing factors [5,10,11], scenarios analysis and prediction of carbon emissions [12][13][14][15], the application of technology and policy simulation to reduce carbon emissions [16][17][18]. Moreover, the studies on influencing factors is a critical field to make projection and reduction policies for carbon emissions [5].…”

mentioning

confidence: 99%

“…It has advantages of comprehensive analysis and is considered as an established carbon emissions model which has been used widely [5,19,28,29].Under the background of carbon emissions peak, several scholars focused on the STIRPAT model to analyze and project carbon emissions, thus assisting Chinese policymakers to formulate reasonable sustainable approach of socio-economic development. These studies suggested that Chinese carbon emissions will peak between 2020 and 2030 [4,14,15,34]. However, the national carbon emissions peak would not mean that provincial emissions status at the regional-level [5].…”

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confidence: 99%

Scenario Analysis of Carbon Emissions in the Energy Base, Xinjiang Autonomous Region, China

et al. 2019

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The realization of carbon emissions peak is important in the energy base area of China for the sustainable development of the socio-economic sector. The STIRPAT model was employed to analyze the elasticity of influencing factors of carbon emissions during 1990-2010 in the Xinjiang autonomous region, China. The results display that population growth is the key driving factor for carbon emissions, while energy intensity is the key restraining factor. With 1% change in population, gross domestic product (GDP) per capita, energy intensity, energy structure, urbanization level, and industrial structure, the change in carbon emissions was 0.80%, 0.48%, 0.20%, 0.07%, 0.58%, and 0.47%, respectively. Based on the results from regression analysis, scenario analysis was employed in this study, and it was found that Xinjiang would be difficult to realize carbon emissions peak early around 2030. Under the condition of the medium-high change rates in energy intensity, energy structure, industrial structure, and with the low-medium change rates in population, GDP per capita, and urbanization level, Xinjiang will achieve carbon emissions peak at of 626.21, 636.24, 459.53, and 662.25 million tons in the year of 2030626.21, 636.24, 459.53, and 662.25 million tons in the year of , 2030626.21, 636.24, 459.53, and 662.25 million tons in the year of , 2040626.21, 636.24, 459.53, and 662.25 million tons in the year of , and 2040 the background of Chinese carbon emissions peak around 2030, this paper puts forward relevant policies and suggestions to the sustainable socio-economic development for the energy base area, Xinjiang autonomous region.Sustainability 2019, 11, 4220 2 of 18 in order to mitigate the risks of global warming, the international community has made great efforts to reduce carbon emissions [4]. As an emerging market, though simultaneous development and accelerating industrialization, informatization, agricultural modernization, and urbanization, due to its long-term growth mode of high consumption, high pollution and low output, China has become the country with the largest carbon emissions [5]. In 2006, Chinese carbon emissions surpassed the United States of America (USA) and became the world's largest emitter [4]. In 2015, China accounted for 28.65% of the world's total carbon emissions, exceeding the sum of the USA (14.93%) and the European Union (9.68%), which was ranked second and third in the world, respectively (Global Carbon Project. http://www.globalcarbonatlas.org/en/ CO 2 emissions). With this, China has incurred huge attention from the world, which posed great international political pressures on China. In face of this situation, China pledged at the Copenhagen Climate Summit to increase the percentage of renewable energy in energy consumption structure to 15%, and to cut 40-45% of the energy intensity in 2020 when compared to the 2005 levels. Moreover, China promised at the Paris Agreement to increase the percentage of renewable energy in energy consumption structure to 20% and to cut 60-65% of the e...

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Carbon emissions mitigation methods for cement industry using a systems dynamics model

Ige,

Von Kallon,

Desai

2024

Clean Techn Environ Policy

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Cement production contributes significantly to anthropogenic greenhouse gas emissions (GHG), a major contributor to global carbon emissions. The environmental impacts of cement production have grown in recent years and it is urgent to reduce its carbon footprint. Systems dynamics (SD) is a simulation method used to understand the nonlinear behavior of complex systems over time. It is commonly used in various sectors to predict emissions and conduct policy experiments. Due to the poor implementation of carbon mitigation strategies within the cement industry, enhancing policymaking by employing more advanced decision-support tools is necessary. This paper reviews previous studies that use the SD approach to assess and compare different mitigation strategies proposed and implemented to reduce carbon emissions in the cement industry. These strategies encompass technological advancements and process improvements, including using alternative fuels and raw materials (adopting low-carbon cementitious materials), energy efficiency improvements, carbon capture and storage and waste heat recovery. The review examines the papers' scope, model descriptions, validation method and mitigation methods highlighted in each study, providing valuable insights for decision makers in the cement industry. Furthermore, the paper discusses the limitations and gaps related to SD modeling, highlighting important factors such as stakeholder engagement in designing effective carbon mitigation strategies. The reviewed studies constantly emphasized technical strategies for mitigating carbon emissions from the cement industry, as stated by the International Energy Agency (IEA) classification. Innovative and emerging technologies, such as WHR, depends on adequate funding, motivation and research and development. However, they frequently neglected to address the barriers hindering their implementation or provide detailed policy measures to overcome them using SD. Additional research is required to assess the practicality and costs of implementing these strategies. Graphical abstract Navigating the way to sustainability in the cement industry: Exploring mitigation strategies through systems dynamics model

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An Agent-Based Model to Project China’s Energy Consumption and Carbon Emission Peaks at Multiple Levels

Cited by 19 publications

References 27 publications

Forecasting the Energy Consumption of China’s Manufacturing Using a Homologous Grey Prediction Model

Forecasting the Energy Consumption of China’s Manufacturing Using a Homologous Grey Prediction Model

Scenario Analysis of Carbon Emissions in the Energy Base, Xinjiang Autonomous Region, China

Carbon emissions mitigation methods for cement industry using a systems dynamics model

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