Carbon emissions index decomposition and carbon emissions prediction in Xinjiang from the perspective of population-related factors, based on the combination of STIRPAT model and neural network

Ziyuan, Chai; Yibo, Yan; Simayi, Zibibula; Yang, Shengtian; Abulimiti, Maliyamuguli; Wang, Yuqing

doi:10.1007/s11356-021-17976-4

Cited by 39 publications

(13 citation statements)

References 85 publications

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“…To promote economic development, the local government chose an unsophisticated development approach in the early stage of development, using energy consumption as the driving force for economic growth, and although the effect of this approach has been significant, carbon emissions are growing rapidly 69 . The carbon emissions in Xinjiang have been increasing rapidly since 2000, with an average annual growth rate of 10.24% 70 .…”

Section: Discussionmentioning

confidence: 99%

Carbon sinks and carbon emissions balance of land use transition in Xinjiang, China: differences and compensation

Luo

Wang

et al. 2022

Sci Rep

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With the continuous enhancement of human activities, the contradiction between regional development and ecological protection is prominent in the ecologically fragile arid areas. It is of great significance for regional sustainable development to understand the ecological supply and demand problems caused by transformation of land using and formulate ecological compensation scheme scientifically. This study takes Xinjiang in China as the research area. It explores the land use transition characteristics and the changes in carbon supply and demand of Xinjiang using methods such as GIS spatial analysis and modified comparative ecological radiation forcing. Finally, the ecological compensation scheme is studied based on the theory of ecological radiation. The research shows that (I) in the study chronology, most of the areas produced only one change in land use. Land use is gradually developing towards the direction of ecological protection. After 2000, grassland recovered well, and 14,298 km2 of other ecological land was transformed into grassland. (II) The change in the carbon sink of the Xinjiang ecosystem first decreased and then increased, and the ecological deficit area started to appear after 2010. The growth of grassland and cropland areas is essential to enhance the carbon sink capacity of arid zones. (III) The amount of ecological compensation in Xinjiang is 31.47 * 108 yuan, and the proportion of the amount received by ecological compensation areas is related to the distance between the supply and demand areas, the amount of carbon sequestration, and the area of the region. This study provides a reference for achieving the healthy development of sustainable land use ecosystems in arid zones.

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Section: Discussionmentioning

confidence: 99%

Carbon sinks and carbon emissions balance of land use transition in Xinjiang, China: differences and compensation

Luo

Wang

et al. 2022

Sci Rep

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“…In this study, we selected the economic and export-related data of Jiangsu Province from 2001 to 2020, built a data model, analyzed the prediction results of GDP values by different forecasting methods, and explored the socioeconomic development trends [21,22]. e empirical results show that the deep learning neural network-based forecasting approach has higher forecasting value and lower forecasting error values than other traditional forecasting approaches, which can provide a basis and reference for macroeconomic regulation and control of the economy and has good practical significance [23]. In addition, a more indepth investigation based on the research of other scholars, through the analysis of GDP-related indicators, can effectively combine GDP and prior indicators, which improves the persuasive power of prediction results and has a very important role in socioeconomic forecasting [24].…”

Section: Discussionmentioning

confidence: 99%

Socioeconomic Development Trend Prediction Model and Research Which is Based on Deep Neural Network

Zhang

Wei²,

Wang³

2022

Mathematical Problems in Engineering

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In recent years, my country’s economic development has entered a new stage, and economic development has entered a new height. Gross Domestic Product (GDP) is the most important and commonly used measure to evaluate social and economic development. It can comprehensively reflect the operating conditions of the economy. At the 2014 APEC summit, Xi Jinping focused on the characteristics of the new normal of China’s economy. The economic growth has undergone major changes, and the economic structure has become more and more complete and has been continuously optimized. Therefore, the driving force of innovation plays an important role in the future social and economic development. The high-tech demonstration zone is the main hub of my country’s economic innovation and scientific and technological progress, and the development of the high-tech demonstration zone is particularly important in the development of the innovative economy. By predicting the economic growth, the development structure can be adjusted in time, and the coordinated allocation of resources can be achieved, which is conducive to the stable and rapid development of the economy. Therefore, it is very necessary to predict my country’s economic growth. At present, more and more scholars have invested more energy in the establishment of economic forecasting models. Previously, the prediction effect of traditional models was not satisfactory. At present, the field of target camera machine learning has been transferred. The application of the powerful nonlinear fitting ability of the machine learning model can improve the prediction effect. Based on this, this paper studies the use of artificial neural network with improved BP algorithm to predict social and economic development, and analyzes social and economic development trends and changing laws. The results show that the forecasting model constructed in this paper has a good forecasting effect, and the research results can provide a theoretical basis for scientifically formulating macro-control policies.

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“…Also, the influence of economic urbanization and technology on carbon emissions were considered to expanded STIRPAT model, and the empirical analysis of this model showed that GDP per capita and population size have positive impact on carbon emissions, and population urbanization, economic urbanization, and energy intensity have inhibitory effects on carbon emission (Liu et al 2021 ). On this basis, the STIRPAT model is applied to determine the drivers of carbon emissions in Xinjiang, and the application results showed that urbanization and per capita GDP have considerable beneficial impacts on carbon emissions (Chai et al 2022 ). Moreover, the drivers of building carbon emissions were explored according to the demand and supply and introduced six identified factors to establish different STIRPAT models, and the empirical analysis showed that the indirect emission intensity and building construction among six identified factors have the greatest impact on carbon emissions (Zhu et al 2022c ).…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting the carbon emissions in Hubei Province under the background of carbon neutrality: a novel STIRPAT extended model with ridge regression and scenario analysis

Rao

Huang

Chen

et al. 2023

Environ Sci Pollut Res

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The impact of global greenhouse gas emissions is increasingly serious, and the development of green low-carbon circular economy has become an inevitable trend for the development of all countries in the world. To achieve emission peak and carbon neutrality is the primary goal of energy conservation and emission reduction. As the core province in central China, Hubei Province is under prominent pressure of carbon emission reduction. In this paper, the future development trend of carbon emissions is analyzed, and the emission peak value and carbon peak time in Hubei Province is predicted. Firstly, the generalized Divisia index method (GDIM) model is proposed to determine the main influencing factors of carbon emissions in Hubei Province. Secondly, based on the main influencing factors identified, a novel STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) extended model with ridge regression is established to predict carbon emissions. Thirdly, the scenario analysis method is used to set the variables of the STIRPAT extended model and to predict the emission peak value and carbon peak time in Hubei Province. The results show that Hubei Province’s carbon emissions peaked first in 2025, with a peak value of 361.81 million tons. Finally, according to the prediction results, the corresponding suggestions on carbon emission reduction are provided in three aspects of industrial structure, energy structure, and urbanization, so as to help government establish a green, low-carbon, and circular development economic system and achieve the industry’s cleaner production and sustainable development of society.

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Carbon emissions index decomposition and carbon emissions prediction in Xinjiang from the perspective of population-related factors, based on the combination of STIRPAT model and neural network

Cited by 39 publications

References 85 publications

Carbon sinks and carbon emissions balance of land use transition in Xinjiang, China: differences and compensation

Carbon sinks and carbon emissions balance of land use transition in Xinjiang, China: differences and compensation

Socioeconomic Development Trend Prediction Model and Research Which is Based on Deep Neural Network

Forecasting the carbon emissions in Hubei Province under the background of carbon neutrality: a novel STIRPAT extended model with ridge regression and scenario analysis

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