Scenario Prediction of Energy Consumption and CO2 Emissions in China’s Machinery Industry

Lin, Boqiang; Liu, Weisheng

doi:10.3390/su9010087

Cited by 11 publications

(4 citation statements)

References 48 publications

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“…For primary industry, the spatial differences mainly depends on fossil energy structure and energy intensity effects. With the process of agricultural mechanization, modern large-scale agricultural machinery has gradually replaced manpower, leading to a dramatic growth of energy consumption in 2040 (Lin and Liu 2017). The energy intensity effect of most provinces is lower than the benchmark except Tianjin, Inner Mongolia, Heilongjiang, and Shanghai.…”

Section: Provincial Decomposition Resultsmentioning

confidence: 99%

Analyzing driving forces of China’s carbon emissions from 1997 to 2040 and the potential emission reduction path: through decomposition and scenario analysis

Song

Zhao

Wang

2021

Clean Techn Environ Policy

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Energy and environmental policies are important methods for the government to restrain carbon emissions growth. Identifying the potential dynamic trends of China's carbon emissions under different scenarios has important reference significance for the government's policy implementation. This paper firstly predicted China's carbon emissions from 2017 to 2040 based on three energy transition scenarios at the industrial level. Then, Logarithmic Mean Divisia Index decomposition model was applied to evaluate the driving forces of emissions changes during 1997-2040. Finally, the Spatial-Temporal Logarithmic Mean Divisia Index model was used to explore the emissions reduction potential and the potential reduction path at provincial level. The results showed that (1) as the reduction in energy intensity cannot offset the growth of industrial scale, the carbon emissions of all industries have shown an increasing trend from 1997 to 2017; (2) In the current policies scenario, China's carbon emissions cannot reach the peak before 2040. And only in the sustainable development scenario, the carbon emissions of the three industries will all reach the peaks before 2030. And the development of non-fossil energy will reduce carbon emissions by more than 30%; (3) Hebei, Shanxi, Inner Mongolia, Ningxia, and Heilongjiang are key provinces and improving energy efficiency of the secondary industry is a potential way to promote carbon emissions reduction.

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Section: Provincial Decomposition Resultsmentioning

confidence: 99%

Analyzing driving forces of China’s carbon emissions from 1997 to 2040 and the potential emission reduction path: through decomposition and scenario analysis

Song

Zhao

Wang

2021

Clean Techn Environ Policy

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“…Wood is characterized by its strong carbon fixation capacity and good stiffness (Wang et al 2014(Wang et al , 2015(Wang et al , 2016(Wang et al , 2018(Wang et al , 2019. Therefore, timber structures naturally have the advantages of being low-carbon and environmentally friendly (Lin et al 2015;Zuo and Wang 2017;Geng 2018;Liu et al 2022). However, the operational phase of a building is highly energy-intensive.…”

Section: Introductionmentioning

confidence: 99%

Simulation calculation and analysis of building energy consumption of multi-story glued laminated timber structures

Zou,

Gu,

Adjei

et al. 2023

BioRes

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As the only renewable building material, wood has a strong carbon sequestration effect. Thus, timber structures have the natural advantages of saving energy and reducing emissions. Currently, the research objects of building energy consumption are rarely timber structures. To further control the operational energy consumption of timber structures, this paper takes six-story glued laminated timber beam-column frame and light wood-frame shear wall structures as the research objects. Building energy consumption research is conducted through testing the heat transfer coefficient of the envelope structure, air circulation ratio, and Building Information Modeling (BIM) technology on-site. The results show that the energy consumption of the building is consistent with the current energy consumption of small- and medium-sized office buildings, and the heat gain and loss of the building are mainly due to solar radiation and heat conduction of the envelope, respectively. The airtightness of the building has the greatest influence on the energy consumption of the building, and the type of building structure and window-to-wall ratio have little influence on the energy consumption of the building.

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“…To solve the 'misplaced replacement' issue of the classical GM(1,1) model, we use the ordinary least-squares (OLS) method and Cramer's rule to estimate parameters a, b and c according to the time response function of the whitenization differential equation, that is Equation (13). Let…”

Section: Homologous Grey Energy Prediction Modelmentioning

confidence: 99%

“…The energy consumption of the manufacturing industry is affected by many uncertain factors, such as industry structure, technology level, energy price, economic scale and national policy [13] and has the typical characteristic of uncertainty, that is 'grey cause' [14]. An econometric regression model (ERM) is an important and frequently-used prediction model, which operates under the premise of a large sample of data (not less than 30), and mainly by studying data statistical laws to find the functional relation among variables.…”

Section: Introductionmentioning

confidence: 99%

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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Scenario Prediction of Energy Consumption and CO2 Emissions in China’s Machinery Industry

Cited by 11 publications

References 48 publications

Analyzing driving forces of China’s carbon emissions from 1997 to 2040 and the potential emission reduction path: through decomposition and scenario analysis

Analyzing driving forces of China’s carbon emissions from 1997 to 2040 and the potential emission reduction path: through decomposition and scenario analysis

Simulation calculation and analysis of building energy consumption of multi-story glued laminated timber structures

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

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