Research on Determining the Critical Influencing Factors of Carbon Emission Integrating GRA with an Improved STIRPAT Model: Taking the Yangtze River Delta as an Example

Guo, Feipeng; Zhang, Linji; Wang, Zifan; Ji, Shaobo

doi:10.3390/ijerph19148791

Cited by 19 publications

(6 citation statements)

References 56 publications

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“…The used models and methods of this study are equally applicable to other regions and researches and/or investigations. For example, an improved STIRPAT model was adopted to explore the critical influencing factors of carbon emission in the Yangtze River Delta [ 67 ], whereas an extended STIRPAT model was employed to analyze the multivariate driving factors of carbon emissions from energy consumption in Xinjiang Province [ 95 ] and the other provincial household carbon emissions in China [ 96 ]. Other research reports employing similar models and methods are found in the web search too.…”

Section: Discussionmentioning

confidence: 99%

“…Carbon sink is the process, activity, or mechanism of absorbing carbon dioxide in the atmosphere through afforestation, vegetation restoration and other measures, so as to reduce concentration of greenhouse gas in the atmosphere [ 62 , 63 , 64 , 65 ]. It is an important part of the ecosystems and an important way to achieve the regional carbon neutrality [ 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 ]. In the long-term grain crop planting process, grain crop production and forest coverage had a significant effect on carbon emission absorption [ 64 , 65 ].…”

Section: Introductionmentioning

confidence: 99%

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The Carbon Emission Characteristics and Reduction Potential in Developing Areas: Case Study from Anhui Province, China

Zhang

Jiang

Jin

et al. 2022

IJERPH

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Global warming and world-wide climate change caused by increasing carbon emissions have attracted a widespread public attention, while anthropogenic activities account for most of these problems generated in the social economy. In order to comprehensively measure the levels of carbon emissions and carbon sinks in Anhui Province, the study adopted some specific carbon accounting methods to analyze and explore datasets from the following suggested five carbon emission sources of energy consumption, food consumption, cultivated land, ruminants and waste, and three carbon sink sources of forest, grassland and crops to compile the carbon emission inventory in Anhui Province. Based on the compiled carbon emission inventory, carbon emissions and carbon sink capacity were calculated from 2000 to 2019 in Anhui Province, China. Combined with ridge regression and scenario analysis, the STIRPAT model was used to evaluate and predict the regional carbon emission from 2020 to 2040 to explore the provincial low-carbon development pathways, and carbon emissions of various industrial sectors were systematically compared and analyzed. Results showed that carbon emissions increased rapidly from 2000 to 2019 and regional energy consumption was the primary source of carbon emissions in Anhui Province. There were significant differences found in the increasing carbon emissions among various industries. The consumption proportion of coal in the provincial energy consumption continued to decline, while the consumption of oil and electricity proceeded to increase. Furthermore, there were significant differences among different urban and rural energy structures, and the carbon emissions from waste incineration were increasing. Additionally, there is an inverted “U”-shape curve of correlation between carbon emission and economic development in line with the environmental Kuznets curve, whereas it indicated a “positive U”-shaped curve of correlation between carbon emission and urbanization rate. The local government should strengthen environmental governance, actively promote industrial transformation, and increase the proportion of clean energy in the energy production and consumption structures in Anhui Province. These also suggested a great potential of emission reduction with carbon sink in Anhui Province.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Carbon Emission Characteristics and Reduction Potential in Developing Areas: Case Study from Anhui Province, China

Zhang

Jiang

Jin

et al. 2022

IJERPH

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“…The STIRPAT model, an important tool for environmental analysis, is derived from the IPAT environmental pressure equation proposed by Ehrlich et al and is widely used to analyze the non-proportional impact of human driving factors on the environment (Ehrlich and Holdren, 1971). In this paper, referring to the practices of Guo et al (2022b), Zhang et al (2023b), Peng et al (2023), andLiu et al (2023), a nonlinear STIRPAT model with multiple independent variables is constructed, and its basic formula is as follows:…”

Section: Analysis On Influencing Factors Of Agricultural Carbon Emiss...mentioning

confidence: 99%

Prediction of agricultural carbon emissions in China based on a GA-ELM model

Guo,

Yang,

Shen

et al. 2023

Front. Energy Res.

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Introduction: Strengthening the early warning of greenhouse gas emissions from agriculture is an important way to achieve Goal 13 of the Sustainable Development Goals. Agricultural carbon emissions are an important part of greenhouse gases, and accelerating the development of green and low-carbon agriculture is of great significance for China to achieve high-quality economic development and the goal of “carbon neutrality in peak carbon dioxide emissions”.Methods: By measuring the total agricultural carbon emissions in China and seven administrative regions from 2000 to 2021, the paper analyzes the influencing factors of agricultural carbon emissions in China by using STIRPAT environmental pressure model, and on this basis, predicts the peak trend of agricultural carbon emissions in China under different development scenarios by using the extreme learning machine model optimized by genetic algorithm.Results: The results showed that the extreme learning machine model improved by the genetic algorithm can overcome the shortcoming that the extreme learning machine model is easy to fall into the local optimal solution, thus obtaining higher prediction accuracy. At the same time, from 2000 to 2021, the total agricultural carbon emissions in China showed a continuous fluctuation trend, and due to the constraints of the agricultural economic level, agricultural industrial structure, and agricultural human capital, the agricultural carbon emissions showed spatial differentiation. It is worth noting that, in the context of green development, the agricultural carbon emissions of the seven regions in China all have the potential to achieve the “peak carbon dioxide emissions” goal in 2030, with only a slight difference at the peak.Discussion: The research results of this paper provide evidence for the government to formulate flexible, accurate, reasonable and appropriate agricultural carbon reduction policies, which is helpful to strengthen the exchanges and cooperation of regional agricultural and rural carbon reduction and fixation, and actively and steadily promote China's agriculture to achieve the goal of “peak carbon dioxide emissions carbon neutrality”.

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“…The first is the Logarithmic Mean Division Index (LMDI) decomposition model, lauded for its distinct analysis pathway, lack of residual, and excellent zero-value processing capacity; hence, it is extensively employed in carbon emission driver decomposition research across multiple fields [14]. The second model is the STIRPAT model, appreciated for its scalability and stochasticity, which has found broad application in energy carbon emission prediction and assessment research [15]. The third method is the Grey Relational Analysis (GRA), a tool used within grey system theory to measure the similarity or heterogeneity among various factors [16].…”

Section: Literature Reviewmentioning

confidence: 99%

The Impact of the Green Economy on Carbon Emission Intensity: Comparisons, Challenges, and Mitigating Strategies

Jia

Fan

et al. 2023

Sustainability

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Global warming, driven primarily by the substantial discharge of greenhouse gases such as carbon dioxide, presents a progressively intensifying concern. To curtail these emissions, the international community is persistently exerting efforts. Traditional economic paradigms have contributed to resource exhaustion and severe pollution, as well as other issues. The green economy, characterized by “eco-friendly”, “low carbon”, and “intensive development” principles, proposes strategies to counter global warming. The current study considers 30 Chinese provinces and cities, assessing coal, coke, and diesel consumption data from 2004 to 2020. Using the carbon emission factor method to quantify carbon emissions, spatial autocorrelation of emissions across various regions is evaluated by employing Moran’s I. The Geographically and Temporally Weighted Regression (GTWR) of carbon emissions and green economy is formulated to scrutinize the contributing factors to carbon emissions, focusing on spatial–temporal evolution and spatial heterogeneity. According to the analysis results, the corresponding suggestions are put forward. This also facilitates analysis of the green economy’s impact on China’s carbon peak and carbon neutrality targets. The findings suggest the following: (1) Over the study period, China’s aggregate carbon emissions exhibited an upward trend, although the growth rate notably decelerated after 2011, and significant spatial clustering of carbon emissions was discerned across the regions. (2) Overall, both economic and social development markedly augmented carbon emission intensity. (3) Spatially, the green economy’s effect on carbon emissions demonstrated significant spatial differentiation. By constructing a GTWR model of the green economy–carbon emission relationship, this study provides a trajectory for regional green sustainability and offers empirical guidance for developing countries grappling with global warming.

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Research on Determining the Critical Influencing Factors of Carbon Emission Integrating GRA with an Improved STIRPAT Model: Taking the Yangtze River Delta as an Example

Cited by 19 publications

References 56 publications

The Carbon Emission Characteristics and Reduction Potential in Developing Areas: Case Study from Anhui Province, China

The Carbon Emission Characteristics and Reduction Potential in Developing Areas: Case Study from Anhui Province, China

Prediction of agricultural carbon emissions in China based on a GA-ELM model

The Impact of the Green Economy on Carbon Emission Intensity: Comparisons, Challenges, and Mitigating Strategies

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