Assessment Framework of Provincial Carbon
Emission Peak Prediction in China: An Empirical
Analysis of Hebei Province

Li, Wei; Du, Lei

doi:10.15244/pjoes/99302

Cited by 18 publications

(8 citation statements)

References 31 publications

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“…This paper constructs a model of the effect of the TDI on the ERI from the classical STIRPAT environmental impact model, which has the following standard form [77][78][79]: The HH-type represents neighborhoods where both the TDI and ERI have high values and symbiotically promote each other's development. This type is primarily concentrated in Shanghai, Zhejiang, and southern Jiangsu regions.…”

Section: Model Constructionmentioning

confidence: 99%

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The Effects of Tourism Development on Eco-Environment Resilience and Its Spatio-Temporal Heterogeneity in the Yangtze River Economic Belt, China

Wang,

Chen,

Lei

et al. 2023

Sustainability

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Tourism sustainability is a significant approach to forming a synergistic model of industry and ecology in ecologically vulnerable areas. Scientifically detecting the effect mechanism of tourism development on eco-environment resilience is important in achieving regional social-ecological system sustainability. In this work, empirical exploration is conducted on the tourism development index (TDI) and eco-environment resilience index (ERI) in the Yangtze River Economic Belt (YREB) to study the spatio-temporal heterogeneity of TDI’s effect on the ERI. The results indicate significant growth in the TDI in the YREB, with the formation of tourist clusters around Shanghai and Chongqing as the core. Although the ERI typically exhibits a declining trend, the rate of decline has notably slowed, forming a “high at the sides and low in the middle” spatial pattern. The TDI and ERI are spatially dependent in the YREB, with predominantly high-high (HH) and low-high (LH) clusters in Shanghai, Zhejiang, and Jiangsu. Conversely, upstream regions with strong eco-environmental foundations exhibit low-low (LL) and high-low (HL) clusters. In general, the TDI promotes the ERI, but there is significant spatio-temporal heterogeneity in the YREB. Positive impact regions are expanding, while negative impact regions are shrinking. These results could provide scientific evidence for differentiated classification and control policies in the YREB.

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Section: Model Constructionmentioning

confidence: 99%

“…This paper constructs a model of the effect of the TDI on the ERI from the classical STIRPAT environmental impact model, which has the following standard form [77][78][79]:…”

Section: The Effect Of the Tdi On The Eri 431 Model Constructionmentioning

confidence: 99%

The Effects of Tourism Development on Eco-Environment Resilience and Its Spatio-Temporal Heterogeneity in the Yangtze River Economic Belt, China

Wang,

Chen,

Lei

et al. 2023

Sustainability

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“…Zhang L et al applied the tabu search (TS) algorithm to the least squares support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to predict the carbon emissions of China's energy consumption, and finally found that its carbon emissions will gradually decrease around 2026 (Zhang et al 2019). STIRPAT model is more accurate in predicting the trend of carbon emission changes, while GA-BP model is more accurate in predicting the numerical value (Li &Du 2019). This result also shows the necessity of exploring the change of transmission mechanism.…”

Section: Literature Reviewmentioning

confidence: 99%

Whether deep learning is an efficient method for carbon emission prediction research?

Yao,

Zhang,

Wang

et al. 2023

Preprint

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Accurately predicting future carbon emissions is of great significance for the government to scientifically promote carbon emission reduction policies. Among the current technologies for forecasting carbon emissions, the most prominent ones are econometric models and deep learning, but few works have systematically compared and analyzed the forecasting performance of the methods. Therefore, the paper make a comparison for deep learning model, machine learning model, and the econometric model to demonstrate whether deep learning is an efficient method for carbon emission prediction research. In model mechanism, neural network for deep learning refers to an information processing model established by simulating biological neural system, and the model can be further extended through bionic characteristics. So the paper further optimizes the model from the perspective of bionics, and proposes an innovative deep learning model based on the memory behavior mechanism of group creatures. Comparison results show that the prediction accuracy of the heuristic neural network is higher than that of the econometric model. Through in-depth analysis, the Heuristic neural network is more suitable for predicting future carbon emissions, while the econometric model is more suitable for clarifying the impact of influencing factors on carbon emissions.

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“…In terms of study regions, the existing literature on CO2 emission projections in China is mainly focused on the national (Fan et al, 2022 ; Jiang et al, 2019 ; Li, 2020 ), multi-province (Fang et al, 2019 ; Zhang et al, 2021 ; Zuo et al, 2020 ), and single-province (Li & Du, 2019 ; Li et al, 2021 ; Ren & Long, 2021 ), and less literature is available on CO2 emission projections from the perspective of metropolitan areas. The only literature that exists is only at the scale of a single metropolitan area (Guo et al, 2018b ; Liu et al, 2022 ), and there is a gap in the literature on CO2 emission projections for Chinese metropolitan areas at the national level.…”

Section: Introductionmentioning

confidence: 99%

A study of the potential for peak carbon dioxide emissions in metropolitan areas: the case of China

Zeng

Chen

2023

Environ Monit Assess

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As cities become increasingly interconnected in production and lifestyles, metropolitan areas have become the main areas for carbon dioxide (CO2) emissions. This article is the first to investigate the potential for peak CO2 emissions in China’s metropolitan areas. Specifically, this study constructs logistic growth models using time series data of CO2 emissions from 1997 to 2017 for 26 metropolitan areas. Secondly, this study combines scenario analysis and the STIRPAT model for projection. Moreover, using the grid search method, this study optimizes ridge regression’s penalty term coefficient (alpha). The results show that most metropolitan areas in China entered the saturation stage of carbon emission in 2016, but it is still challenging to achieve the goal of peak carbon dioxide emission by 2030. Per capita GDP, the proportion of the secondary industry, population, and urbanization positively affects CO2 emissions, and green technology innovation negatively affects CO2 emissions. Therefore, to achieve the peak CO2 emissions target, China should develop differentiated carbon reduction strategies for metropolitan areas and focus on the optimization and upgrading of industrial structure, the improvement of green technology level, and the low carbonization of residents’ lifestyles in metropolitan areas. The significance of this study is that it helps policymakers to project potential peak CO2 emission trajectories from a bottom-up perspective, and its findings provide insights into China’s peak CO2 emissions pathway. Supplementary Information The online version contains supplementary material available at 10.1007/s10661-023-11371-x.

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Assessment Framework of Provincial Carbon Emission Peak Prediction in China: An Empirical Analysis of Hebei Province

Cited by 18 publications

References 31 publications

The Effects of Tourism Development on Eco-Environment Resilience and Its Spatio-Temporal Heterogeneity in the Yangtze River Economic Belt, China

The Effects of Tourism Development on Eco-Environment Resilience and Its Spatio-Temporal Heterogeneity in the Yangtze River Economic Belt, China

Whether deep learning is an efficient method for carbon emission prediction research?

A study of the potential for peak carbon dioxide emissions in metropolitan areas: the case of China

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