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Balancing Energy Consumption and Carbon Dioxide Emission under the Context of Carbon Peak in China

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 14318

Special Issue Editor


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Guest Editor
College of Management Engineering and Business, Hebei University of Engineering, Handan 056038, China
Interests: energy consumption; carbon dioxide emission; carbon peak; grey uncertain theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global warming caused by excessive carbon emissions has disrupted the Earth’s ecosystem. In response to climate change, the Chinese government has put forward specific goals for carbon peaking by 2030 and neutrality by 2060. In order to reduce carbon emission, balancing energy consumption and carbon dioxide emission is a hot topic. Balancing energy consumption and economic development is very important. This Special Issue aims to contribute to this important topic and welcomes theoretical and empirical research that finds a road to sustainable development in China. Specific topics include, but are not limited to: carbon neutrality, grey forecasting model of energy consumption and carbon dioxide emission, uncertain model of sustainable development, environmental management, impact of carbon peaking and carbon neutrality, impact of population on carbon peaking, grey evaluation of carbon dioxide emission, new energy, and national or regional energy policies.

I look forward to receiving your contributions.

Prof. Dr. Lifeng Wu
Guest Editor

Manuscript Submission Information

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Keywords

  • new energy
  • carbon neutrality
  • grey forecasting model of energy consumption
  • grey forecasting model of carbon dioxide emission
  • uncertain model of sustainable development
  • impact of carbon peaking
  • impact of carbon neutrality
  • impact of population on carbon peaking
  • grey evaluate of carbon dioxide emission
  • national or regional energy policies

Published Papers (9 papers)

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Research

17 pages, 2059 KiB  
Article
Long-Term Prediction of Hydrometeorological Time Series Using a PSO-Based Combined Model Composed of EEMD and LSTM
by Guodong Wu, Jun Zhang and Heru Xue
Sustainability 2023, 15(17), 13209; https://doi.org/10.3390/su151713209 - 3 Sep 2023
Cited by 1 | Viewed by 1025
Abstract
The accurate long-term forecasting of hydrometeorological time series is crucial for ensuring the sustainability of water resources, environmental conservation, and other related fields. However, hydrometeorological time series usually have strong nonlinearity, non-stationarity, and complexity. Therefore, it is extremely challenging to make long-term forecasts [...] Read more.
The accurate long-term forecasting of hydrometeorological time series is crucial for ensuring the sustainability of water resources, environmental conservation, and other related fields. However, hydrometeorological time series usually have strong nonlinearity, non-stationarity, and complexity. Therefore, it is extremely challenging to make long-term forecasts of hydrometeorological series. Deep learning has been widely applied in time series prediction across various fields and exhibits exceptional performance. Among the many deep learning techniques, Long Short-Term Memory (LSTM) neural networks possess robust long-term predictive capabilities for time series analysis. Signal decomposition technology is utilized to break down the time series into multiple low complexity and highly stationary sub-sequences, which are then individually trained using LSTM before being reconstructed to generate accurate predictions. This approach has significantly advanced the field of time series prediction. Therefore, we propose an EEMD-LSTM-PSO model, which employs Ensemble Empirical Mode Decomposition (EEMD), to decompose the hydrometeorological time series and subsequently construct an LSTM model for each component. Furthermore, the Particle Swarm Optimization (PSO) algorithm is utilized to optimize the coefficients and reconstruct the final prediction outcomes. The performance of the EEMD-LSTM-PSO model is evaluated by comparing it with four other models using four evaluation indicators: root mean square error (RMSE), mean absolute percentage error (MAPE), correlation coefficient (R), and Nash coefficient (NSE) on three real hydrometeorological time series. The experimental results show that the proposed model exhibits exceptional performance compared with the other four models, and effectively predicts long-term hydrometeorological time series. Full article
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15 pages, 1132 KiB  
Article
The Evolution of the Collaborative Environmental Governance Network in Guizhou Province, China
by Zhanjie Wang, Yongfeng Ma, Shasha Wang, Chaoliang Luo and Yongjian Wang
Sustainability 2023, 15(13), 10012; https://doi.org/10.3390/su151310012 - 24 Jun 2023
Cited by 3 | Viewed by 1353
Abstract
China has successively set goals of carbon peaking and carbon neutrality, aiming to transform to a green and low-carbon economy. Collaborative environmental governance (CEG) is an important way to achieve this transformation. To improve collaborative environmental governance, the study of the evolution of [...] Read more.
China has successively set goals of carbon peaking and carbon neutrality, aiming to transform to a green and low-carbon economy. Collaborative environmental governance (CEG) is an important way to achieve this transformation. To improve collaborative environmental governance, the study of the evolution of the collaborative environmental governance network (CEGN) is necessary. In this study, the CEGNs in different periods in Guizhou Province, China are established based on social network analysis. Then, the evolution of environment governance policy implementers’ functions in the network is investigated by two-dimensional (breadth and intensity) matrices. Moreover, the evolution of core implementers’ functions and its impacts on the operation of the network are analyzed based on the network stability. The results show that the network size, frequency of collaboration, and cohesion have gradually increased, and the network structure has changed from a loose pattern to an expanded and balanced system. Implementers such as the governments of municipalities (prefectures) (MGs) and counties (districts) (CGs), urban management committee (UMCs), and municipal (prefecture) ecological environment bureaus (MBEEs) have played leading and coordinating roles at different phases. However, the cohesion of the network is low, indicating a low level of collaboration. This study integrates the collaborative governance theory and social network analysis, which provides a new way for the study of CEG. In addition, the CEG effect is evaluated by analyzing the evolution of the CEGN, which provides a reference for the CEG policy making in developing regions. Full article
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20 pages, 2301 KiB  
Article
A Simulation Study on Peak Carbon Emission of Public Buildings—In the Case of Henan Province, China
by Hui Li, Yanan Zheng, Guan Gong and Hongtao Guo
Sustainability 2023, 15(11), 8638; https://doi.org/10.3390/su15118638 - 26 May 2023
Cited by 3 | Viewed by 1528
Abstract
With the continuous development of the social economy, carbon emissions from various buildings are increasing. As the most important category of building carbon emissions, the rapid peaking of public buildings is an important part of achieving carbon peak and carbon neutrality. This paper [...] Read more.
With the continuous development of the social economy, carbon emissions from various buildings are increasing. As the most important category of building carbon emissions, the rapid peaking of public buildings is an important part of achieving carbon peak and carbon neutrality. This paper is based on the industrial background of the energy consumption structure of Henan Province, a central province in the developing country of China. Firstly, the energy consumption intensity of buildings and public buildings in Henan Province from 2010 to 2020 was calculated according to the energy balance sheet. The Kaya–LMDI decomposition method was also used to analyse the carbon emissions of public buildings, determining the impact of each influencing parameter on public buildings. Secondly, the scenario prediction model Monte Carlo was run 100,000 times to set the stochastic parameters of the variables in the model to predict the time of carbon peak and carbon emissions. The analysis results indicated that: ① Carbon emissions in Henan Province have exhibited a steady growth trend, increasing from 1533 t in 2010 to 6561 t in 2020; ② The primary factors influencing carbon emissions of public buildings in Henan Province were urbanization rate, public floor area per capita, and energy intensity per unit of public floor area; and ③ Carbon emissions of public buildings in Henan Province followed an inverted U-shaped distribution and are expected to peak at approximately 7423 t by the year 2035. The research method in this paper can guide the simulation study of peak carbon emission prediction in Henan Province based on the influencing parameters of carbon emission from different types of buildings. Moreover, the results of this paper can provide a reference for a more precise study of building carbon reduction in similar regions of developing countries. Full article
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19 pages, 2088 KiB  
Article
Temporal and Spatial Divergence of Embodied Carbon Emissions Transfer and the Drivers—Evidence from China’s Domestic Trade
by Chunli Jin, Qiaoqiao Zhu and Hui Sun
Sustainability 2023, 15(9), 7692; https://doi.org/10.3390/su15097692 - 8 May 2023
Cited by 1 | Viewed by 1259
Abstract
To understand the embodied carbon transfer in China’s domestic trade from 2007 to 2017 and its driving forces, we quantitatively measured the embodied carbon transfer among 30 provinces by using the Multi-regional input-output (MRIO) model, explored the temporal and spatial evolutionary features of [...] Read more.
To understand the embodied carbon transfer in China’s domestic trade from 2007 to 2017 and its driving forces, we quantitatively measured the embodied carbon transfer among 30 provinces by using the Multi-regional input-output (MRIO) model, explored the temporal and spatial evolutionary features of the interprovincial embodied carbon emission transfer by using spatial autocorrelation, and further revealed its drivers using the Geographical Detector Model for the first time. We find that: (1) Based on the producer and consumer accounting principles, the amount of embodied carbon emissions of each province has increased, and there are huge differences. (2) The number of provinces with net embodied carbon emissions transfer is increasing, to 18 in 2017 and the target provinces are mostly energy-rich regions, such as Shanxi, Xinjiang, and Inner Mongolia, which have a severe “carbon leakage” phenomenon with developed coastal provinces. (3) The scale and spatial distribution of net carbon transfer out shows a characteristic of “high in the north and low in the south”, and the tendency of net transfer from the less developed provinces to the developed regions is becoming more and more obvious. (4) The global differences in the promoting factors of the net embodied carbon transfer are not prominent, but the differences at the local scale are significant, with energy intensity and environmental regulation playing an increasingly significant role. Therefore, it is recommended to strengthen low-carbon technology innovation and environmental regulation, increase the percentage of renewable energy consumption, accelerate the mobility of various resource factors, and improve energy utilization efficiency. Full article
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20 pages, 1084 KiB  
Article
The Path to Low Carbon: The Impact of Network Infrastructure Construction on Energy Conservation and Emission Reduction
by Shuangcheng Luo and Yangli Yuan
Sustainability 2023, 15(4), 3683; https://doi.org/10.3390/su15043683 - 16 Feb 2023
Cited by 13 | Viewed by 2183
Abstract
Energy conservation and emission reduction are important ways to cope with global warming. An analysis of energy conservation and emission reduction from the perspective of network infrastructure construction provides an important perspective for the study of sustainable development. Based on the research sample [...] Read more.
Energy conservation and emission reduction are important ways to cope with global warming. An analysis of energy conservation and emission reduction from the perspective of network infrastructure construction provides an important perspective for the study of sustainable development. Based on the research sample of 263 cities in China from 2006 to 2019, and taking the policy of “Broadband China” as a quasi-natural experiment, this paper uses the double difference model to evaluate the impact of network infrastructure construction on energy conservation and emission reduction. The results show that (1) the construction of network infrastructure can significantly improve the energy utilization rate and reduce carbon emissions intensity, which helps to promote energy conservation and emission reduction. (2) From the perspective of a functional mechanism, on the one hand, network infrastructure construction affects energy conservation and emission reduction through micro-mechanisms such as green technology innovation and energy efficiency. On the other hand, network infrastructure construction also drives the development of the Internet and the digital economy, and promotes energy conservation and emission reduction through macro-mechanisms such as industrial structure and financial development. (3) The heterogeneity analysis shows that network infrastructure construction in non-resource-based cities, eastern regions and low-carbon cities has a greater impact on energy conservation and emission reduction. This study provides a new perspective for achieving low-carbon development goals. Full article
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16 pages, 2113 KiB  
Article
Analysis of Air Quality Evolution Trends in the Chinese Air Pollution Transmission Channel Cities under Socioeconomic Development Scenarios
by Kaihe Shi, Huiru Liu, Li Zhang and Qing Li
Sustainability 2023, 15(3), 2118; https://doi.org/10.3390/su15032118 - 22 Jan 2023
Cited by 2 | Viewed by 1434
Abstract
In this paper, based on the traditional grey multivariate convolutional model, the concept of a buffer operator is introduced to construct a single-indicator buffered grey multivariate convolutional model applicable to air quality prediction research. The construction steps of the model are described in [...] Read more.
In this paper, based on the traditional grey multivariate convolutional model, the concept of a buffer operator is introduced to construct a single-indicator buffered grey multivariate convolutional model applicable to air quality prediction research. The construction steps of the model are described in detail in this paper, and the stability of the model is analyzed based on perturbation theory. Furthermore, the model was applied to predict the air quality composite index of the “2 + 26” Chinese air pollution transmission corridor cities based on different socioeconomic development scenarios in a multidimensional manner. The results show that the single-indicator buffered grey multivariate convolutional model constructed in this paper has better stability in predicting with a small amount of sample data. From 2020 to 2025, the air quality of the target cities selected in this paper follows an improving trend. The population density, secondary industry, and urbanization will not have a significant negative impact on the improvement of air quality if they are kept stable. In the case of steady development of secondary industry, air quality maintained a stable improvement in 96.4% of the “2 + 26” cities. The growth rate of population density will have an inverted U-shaped relationship with the decline in the city air quality composite index. In addition, with the steady development of urbanization, air quality would keep improving steadily in 71.4% of the “2 + 26” cities. Full article
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21 pages, 1924 KiB  
Article
Evolutionary Game Mechanism of Governmental Cross-Regional Cooperation in AirPollution Management
by Na Zhang, Haiyan Wang, Baohua Yang and Muyuan Wu
Sustainability 2023, 15(2), 1413; https://doi.org/10.3390/su15021413 - 11 Jan 2023
Viewed by 1543
Abstract
Intergovernmental collaboration is essential for regionally coordinated air pollution prevention and control. An evolutionary game model of local government behavior strategy selection taking into account the fixed cost of the local government, the total amount of network production factors, and the total amount [...] Read more.
Intergovernmental collaboration is essential for regionally coordinated air pollution prevention and control. An evolutionary game model of local government behavior strategy selection taking into account the fixed cost of the local government, the total amount of network production factors, and the total amount of total network factors of production that can be moved is built based on the social capital theory in order to realize the cross-border collaborative control of regional air pollution. The issue of an intergovernmental cooperation framework for collaborative prevention and control of large-scale air pollution is addressed from the standpoint of the multi-stakeholder “benefit–cost” drive. Additionally, the major variables influencing the behavioral approach selection for intergovernmental cooperation are considered. The initial sensitivity of the evolution path of the local government behavioral strategy is also analyzed. The results of this study are: (1) The primary elements impacting intergovernmental cooperation on joint prevention and control of air pollution are fixed costs and fixed benefits, and reducing the fixed costs of such cooperation in an appropriate manner without compromising local governments’ pollution control can do so. (2) Under the assumption that local governments have fixed expenses, the total amount of network factors of production and total network factors of production that can be moved factors of production have a direct impact on intergovernmental cooperation. When local governments’ fixed costs are constant, they are more likely to choose the cooperative behavior option if the sum of their network production factors and total network factors of production that can be moved is higher. (3) The initial probability of cooperation among the three parties and the total amount of production factors have an impact on the system’s ESS when local governments in the area have equal total production factors. The study’s findings can offer theoretical justification for the “profit-driven” intergovernmental coordination of joint prevention and management of air pollution. Full article
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14 pages, 3865 KiB  
Article
Monthly Wind Power Forecasting: Integrated Model Based on Grey Model and Machine Learning
by Xiaohui Gao
Sustainability 2022, 14(22), 15403; https://doi.org/10.3390/su142215403 - 19 Nov 2022
Cited by 2 | Viewed by 1337
Abstract
Wind power generation has been developed rapidly due to rising global interest in renewable clean energy sources. Accurate prediction of the potential amount of such energy is of great significance to energy development. As wind changes greatly by season, time series analysis is [...] Read more.
Wind power generation has been developed rapidly due to rising global interest in renewable clean energy sources. Accurate prediction of the potential amount of such energy is of great significance to energy development. As wind changes greatly by season, time series analysis is considered as a natural approach to characterize the seasonal fluctuation and exponential growth. In this paper, a dual integrated hybrid model is presented by using random forest (RF) to incorporate the extreme gradient boosting (XGB) with empirical mode decomposition (EMD) and a fractional order accumulation seasonal grey model (FSGM). For seasonal fluctuation in vertical dimension processing, the time series is decomposed into high and low frequency components. Then, high and low frequency components are predicted by XGB and extreme learning machine (ELM), respectively. For the exponential growth in horizontal dimension processing, the FSGM is applied in the same month in different years. Consequently, the proposed model can not only be used to capture the exponential growth trend but also investigate the complex high-frequency variation. To validate the model, it is applied to analyze the characteristics of wind power time series for China from 2010 to 2020, and the analysis results from the model are compared with popularly known models; the results illustrate that the proposed model is superior to other models in examining the characteristics of the wind power time series. Full article
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17 pages, 705 KiB  
Article
The Impact of Environmental Tax Reform on Total Factor Productivity of Heavy-Polluting Firms Based on a Dual Perspective of Technological Innovation and Capital Allocation
by Xu He and Qin-Lei Jing
Sustainability 2022, 14(22), 14946; https://doi.org/10.3390/su142214946 - 11 Nov 2022
Cited by 4 | Viewed by 1653
Abstract
As an essential reform of China’s environmental regulatory policy, the environmental protection tax reform achieves a smooth transition from the emission fee system to the environmental protection tax system according to the principle of tax burden leveling. With the quasi-natural experiment of the [...] Read more.
As an essential reform of China’s environmental regulatory policy, the environmental protection tax reform achieves a smooth transition from the emission fee system to the environmental protection tax system according to the principle of tax burden leveling. With the quasi-natural experiment of the introduction of the Environmental Protection Tax Law, this paper examines the effect of environmental protection tax reform on total factor productivity of heavily polluting firms using a difference-in-difference approach based on empirical evidence of Chinese listed companies from 2015 to 2020. It is found that environmental tax reform can significantly increase the level of total factor productivity of heavily polluting firms, and the results remain robust to robustness tests using the OP method, the GMM method to re-measure the total factor productivity of heavily polluting enterprises and the use of different industry classification criteria, with the mechanism of action mainly coming from the technological innovation effect and capital allocation optimization. In addition, the effect of environmental tax reform on total factor productivity of heavily polluting firms is heterogeneous across regions and industries, with the total factor productivity of firms in heavily polluting industries in the eastern region being least affected by environmental tax policies and state-owned enterprises with heavy property rights structures being most affected by environmental tax reform. Full article
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