scholarly journals The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1380
Author(s):  
Huafang Huang ◽  
Xiaomao Wu ◽  
Xianfu Cheng
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Carbon Emissions ◽  
Yangtze River ◽  
Carbon Emission ◽  
Short Term Memory ◽  
Support Vector ◽  
The Yangtze River ◽  
Economic Zone ◽  
Carbon Peak

This study aimed to respond to the national “carbon peak” mid-and long-term policy plan, comprehensively promote energy conservation and emission reduction, and accurately manage and predict carbon emissions. Firstly, the proposed method analyzes the Yangtze River Economic Belt as well as its “carbon peak” and carbon emissions. Secondly, a support vector regression (SVR) machine prediction model is proposed for the carbon emission information prediction of the Yangtze River Economic Zone. This experiment uses a long short-term memory neural network (LSTM) to train the model and realize the experiment’s prediction of carbon emissions. Finally, this study obtained the fitting results of the prediction model and the training model, as well as the prediction results of the prediction model. Information indicators such as the scale of industry investment, labor efficiency output, and carbon emission intensity that affect carbon emissions in the “Yangtze River Economic Belt” basin can be used to accurately predict the carbon emissions information under this model. Therefore, the experiment shows that the SVR model for solving complex nonlinear problems can achieve a relatively excellent prediction effect under the training of LSTM. The deep learning model adopted herein realized the accurate prediction of carbon emission information in the Yangtze River Economic Zone and expanded the application space of deep learning. It provides a reference for the model in related fields of carbon emission information prediction, which has certain reference significance.

scholarly journals Study on Measurement of Green Productivity of Tourism in the Yangtze River Economic Zone, China

2018 ◽  
Vol 10 (8) ◽  
pp. 2786 ◽  
Author(s):  
Gang Liu ◽  
Pengfei Shi ◽  
Feng Hai ◽  
Yi Zhang ◽  
Xingming Li
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Yangtze River ◽  
Carbon Emission ◽  
The Yangtze River ◽  
Bottom Up ◽  
The Past ◽  
Green Productivity ◽  
Green Development ◽  
Economic Zone

This paper introduces energy consumption and carbon emission into the analysis framework of the green productivity of tourism. By comparing and analyzing the two main methods used to evaluate the energy consumption and carbon emission estimations of tourism, namely, the “top-down” and “bottom-up” method, and considering the availability of data, the “bottom-up” method was adopted to evaluate the energy consumption and carbon emissions of tourism in the Yangtze River Economic Zone (YREZ). Then, using the Malmquist-Luenberger (ML) index in the super-efficiency data envelopment analysis (DEA) model, the green productivity of the tourism in 11 provinces and cities in the YREZ from 2006 to 2015 was measured. The empirical results show that: (1) The energy consumption and carbon emissions of tourism in the YREZ have increased steadily over the past 10 years, which has caused a certain degree of pollution to the environment, indicating that tourism is no longer a “smoke-free industry”; (2) there are significant provincial differences between the energy consumption and carbon emissions of tourism in the YREZ, with Shanghai always ranking first, while Guizhou and Yunnan ranks last, which represents that the tourism economic development level is positively correlated with the tourism energy consumption and carbon emissions; (3) the green productivity of tourism in the YREZ shows a fluctuating increasing trend in the past 10 years, and technological progress has become the main reason for its growth in green productivity; and (4) the green productivity of tourism in 11 provinces and cities in the YREZ can be divided into three types: Progressive type of tourism green development, stagnant type of tourism green development, and declining type of tourism green development. Consequently, different types of provinces should explore effective dependency paths based on their own conditions.

scholarly journals Multi-Perspective Influence Mechanism Analysis and Multi-Scenario Prediction of Carbon Emissions——A Case of the Yangtze River Delta, China

2019 ◽  
Vol 118 ◽  
pp. 04014
Author(s):  
Tao Yi ◽  
Mohan Qiu ◽  
Zhengang Zhang ◽  
Song Mu ◽  
Yu Tian
Keyword(s):  
Carbon Emissions ◽  
Emission Reduction ◽  
Yangtze River ◽  
Carbon Emission ◽  
Yangtze River Delta ◽  
Mechanism Analysis ◽  
The Yangtze River ◽  
Carbon Emission Reduction ◽  
Influence Mechanism ◽  
The Yangtze River Delta

Under the mandatory push of meeting carbon emission reduction commitments proposed in the Paris Agreement, the analysis on the peaking time of China’s carbon emissions deserves enough attention. This paper focuses on the peaking times of total carbon emissions (TCE) and carbon emission intensity (CEI) in the Yangtze River Delta (YRD). According to the development of carbon emissions in YRD and related targets in the 13th Five-Year Plan, the peaking times of TCE and CEI in different scenarios are predicted based on the influence mechanism analysis of carbon emissions in YRD from the perspective of energy, economy and society. Considering the development characteristics of China at this stage, this paper introduces several new indicators such as full-time equivalent of research and development (R&D) personnel and investment in environmental pollution control. Based on the study results, several policy recommendations are put forward to fulfil China’s carbon emission reduction commitments.

scholarly journals Study on Total Factor Carbon Emission Efficiency in the Yangtze River Economic Zone, China

2020 ◽  
Vol 143 ◽  
pp. 02026
Author(s):  
Jiwen Chen ◽  
Zuxu Zou
Keyword(s):  
Emission Reduction ◽  
Yangtze River ◽  
Carbon Emission ◽  
Malmquist Index ◽  
The Yangtze River ◽  
Growth Trend ◽  
Reduction Model ◽  
Emission Efficiency ◽  
Economic Zone ◽  
Modernization Process

With the continuous acceleration of the modernization process, the Eco-environmental problems of the Yangtze River Economic Zone in China have become increasingly prominent, which makes the study of carbon emission efficiency become a long-term concern. Based on the panel data of 11 provinces and cities of the Yangtze River Economic Zone in 2009～2016, this paper calculates the DEA-Malmquist index of the Total Factor Carbon Emission Efficiency containing undesirable output in various provinces and cities and three major regions. By studying the DEA-Malmquist index and its decomposition, the results show that the Total Factor Carbon Emission Efficiency of various regions in the Yangtze River Economic Zone presents a growth trend, and its main contribution comes from technological progress. In the future, the emission reduction rules of the Yangtze River Economic Zone will be transformed from the traditional top-down emission reduction model to the bottom-up “independent contribution” emission reduction model.

scholarly journals A Comprehensive Evaluation of Carbon Emission Reduction Capability in the Yangtze River Economic Belt

2020 ◽  
Vol 17 (2) ◽  
pp. 545 ◽  
Author(s):  
Decai Tang ◽  
Yan Zhang ◽  
Brandon J Bethel
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Emission Reduction ◽  
Yangtze River ◽  
Carbon Emission ◽  
The Yangtze River ◽  
Low Carbon ◽  
Carbon Sinks ◽  
Carbon Emission Reduction ◽  
Low Carbon Economy

The Yangtze River Economic Belt (YREB) is an essential part of China’s goal of reducing its national carbon emissions. Focusing on economic and social development, the development of science and technology, carbon sinks, energy consumption, and carbon emissions, this paper uses “the Technique for Order of Preference by Similarity to Ideal Solution mode” (TOPSIS) and “an obstacle factor diagnosis method” to measure the reduction capacity of each province and municipality of the YREB. Key obstacles to achieving the goal of carbon emission reduction are also identified. The main finding is that the emission reduction capacities of Shanghai, Jiangsu and Zhejiang in China’s east is far greater than that of all other provinces and municipalities, the main obstacle of Shanghai, Jiangsu, and Zhejiang are carbon sinks, energy consumption and carbon emission, and other provinces and municipalities are social and economic development. Taking into consideration those evaluation results and obstacles, paths for carbon emission reduction are delineated through a four-quadrant matrix method with intent to provide suitable references for the development of a low-carbon economy in the YREB.

scholarly journals Impact of Land Urbanization on Carbon Emissions in Urban Agglomerations of the Middle Reaches of the Yangtze River

2021 ◽  
Vol 18 (4) ◽  
pp. 1403
Author(s):  
Di Zhang ◽  
Zhanqi Wang ◽  
Shicheng Li ◽  
Hongwei Zhang
Keyword(s):  
Economic Growth ◽  
Carbon Emissions ◽  
Yangtze River ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Urban Expansion ◽  
The Yangtze River ◽  
Identity Model ◽  
Kaya Identity ◽  
Land Urbanization

The urban agglomerations in the middle reaches of the Yangtze River (MYR-UA) are facing a severe challenge in reducing carbon emissions while maintaining stable economic growth and prioritizing ecological protection. The energy consumption related to land urbanization makes an important contribution to the increase in carbon emissions. In this study, an IPAT/Kaya identity model is used to understand how land urbanization affected carbon emissions in Wuhan, Changsha, and Nanchang, the three major cities in the middle reaches of the Yangtze River, from 2000 to 2017. Following the core idea of the Kaya identity model, sources of carbon emissions are decomposed into eight factors: urban expansion, economic level, industrialization, population structure, land use, population density, energy intensity, and carbon emission intensity. Furthermore, using the Logarithmic Mean Divisia Index (LMDI), we analyze how the different time periods and time series driving forces, especially land urbanization, affect regional carbon emissions. The results indicate that the total area of construction land and the total carbon emissions increased from 2000 to 2017, whereas the growth in carbon emissions decreased later in the period. Energy intensity is the biggest factor in restraining carbon emissions, followed by population density. Urban expansion is more significant than economic growth in promoting carbon emissions, especially in Nanchang. In contrast, the carbon emission intensity has little influence on carbon emissions. Changes in population structure, industrial level, and land use vary regionally and temporally over the different time period.

scholarly journals Influence of Urbanization and Foreign Direct Investment on Carbon Emission Efficiency: Evidence from Urban Clusters in the Yangtze River Economic Belt

2021 ◽  
Vol 13 (5) ◽  
pp. 2722
Author(s):  
Shijian Wu ◽  
Kaili Zhang
Keyword(s):  
Economic Development ◽  
Foreign Direct Investment ◽  
Carbon Emissions ◽  
Direct Investment ◽  
Yangtze River ◽  
Carbon Emission ◽  
The Yangtze River ◽  
Urban Agglomerations ◽  
Emission Efficiency ◽  
The Impact

Reducing carbon emissions and realizing green, circular, and low-carbon development is essential for high-quality economic development. Following the construction of a superefficiency SBM model and combining the panel data of three major urban agglomerations in the Yangtze River Economic Belt from 2003 to 2017, carbon emission efficiency was measured and analyzed. A spatial Durbin model (SDM) was incorporated to analyze the urban agglomerations in the Yangtze River Economic Belt and the impact of urbanization quality and foreign direct investment (FDI) on carbon emission efficiency. Finally, the SDM model was used to decompose the spillover effect. Generally, carbon emission efficiency in the three major urban agglomerations in the Yangtze River Economic Belt is low, with regional differences. FDI only has a positive impact on the carbon emissions of the Yangtze River Delta and the middle reaches of the Yangtze River. Furthermore, urbanization and population density have led to high levels of carbon emission in the region; however, the industrial structure and energy intensity factors have inhibited the improvement of regional carbon emission efficiency. Improving the quality of urbanization and trade structure is important to achieve energy conservation and emission reductions, which are pillars of sustainable economic development.

scholarly journals Threshold Effect of Foreign Direct Investment and Carbon Emissions Performance From the Perspective of Marketization Level: Implications for the Green Economy

2021 ◽  
Vol 12 ◽  
Author(s):  
Hao Hu ◽  
Haiyan Wang ◽  
Shuang Zhao ◽  
Xun Xi ◽  
Lan Li ◽  
...  
Keyword(s):  
Foreign Direct Investment ◽  
Foreign Investment ◽  
Carbon Emissions ◽  
Direct Investment ◽  
Yangtze River ◽  
Carbon Emission ◽  
Threshold Effect ◽  
Carbon Intensity ◽  
The Yangtze River ◽  
Low Carbon

Exploring the path and mechanism of marketization level in the effect of foreign direct investment (FDI) on carbon emission performance will help to maximize the stimulation effect of foreign investment on green and low-carbon development. This study used the panel data of 11 provinces and cities in the Yangtze River Economic Belt from 2008 to 2016. A panel threshold model is constructed to explore the non-linear relationship between FDI and carbon emissions performance from the perspective of marketization level. The main conclusions are as follows: First, from the perspective of marketization level, a significant double threshold effect exists between foreign participation and carbon emission intensity, with threshold values of 4.4701 and 9.2516 respectively. Second, as the marketization level increases, the technology spillover effect of FDI increases, and the stimulation effect of foreign participation on carbon intensity decreases significantly, but it does not inhibit carbon intensity, indicating that the overall benefits brought by FDI technology spillovers are still insufficient to offset pollution caused by foreign investment. Third, the eastern region of the Yangtze River Economic Belt has crossed the second threshold. In the central and western regions, the marketization process is relatively slow except for Chongqing, and the regions are still firmly stuck between the first and second thresholds. In response to the conclusions of the empirical research, relevant policy suggestions are put forward from three dimensions, namely, the strategy of introducing foreign investment, construction of the marketization system, and environmental regulation, to achieve low-carbon and green development in the Yangtze River Economic Belt.

scholarly journals Land-Use Carbon Emissions Estimation for the Yangtze River Delta Urban Agglomeration Using 1994–2016 Landsat Image Data

2018 ◽  
Vol 10 (9) ◽  
pp. 1334 ◽  
Author(s):  
Yifan Cui ◽  
Long Li ◽  
Longqian Chen ◽  
Yu Zhang ◽  
Liang Cheng ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Autocorrelation ◽  
Carbon Emissions ◽  
Yangtze River ◽  
Carbon Emission ◽  
Image Data ◽  
Yangtze River Delta ◽  
Carbon Intensity ◽  
The Yangtze River ◽  
The Yangtze River Delta

The amount and growth rate of carbon emissions have been accelerated on a global scale since the industrial revolution (1800), especially in recent decades. This has resulted in a significant influence on the natural environment and human societies. Therefore, carbon emission reduction receives continuously increasing public attention and has long been under debate. In this study, we made use of the land-use specific carbon emission coefficients from previous studies and estimated the land-use carbon emissions and carbon intensities of the Yangtze River Delta Urban Agglomeration (YRDUA)—which consists of 26 eastern Chinese cities—from Landsat image data and socio-economic statistics in 1995, 2005, and 2015. In addition, spatial autocorrelation models including both global and local Moran’s I were used to analyze the spatial autocorrelation of carbon emissions and carbon intensities. It was found that (1) the YRDUA witnessed a rapidly increasing trend for net carbon emissions and a decreasing trend for carbon intensity over the two decades; (2) the spatial differences in carbon intensity had gradually narrowed, but were large in carbon emissions and had gradually increased; and (3) the carbon emissions in 2005 and 2015 had significant spatial autocorrelations. We concluded that (1) from 1995 to 2015 in the YRDUA, carbon emissions were large for the cities along the Yangtze River and carbon intensities were high for Anhui province’s resource-based cities, while both carbon emissions and carbon intensities were small for Zhejiang province’s cities; (2) over two decades, the increase in carbon emissions from urban land was approximately twice the increase in urban land area. Our study can provide useful insights into the assignment of carbon reduction tasks within the YRDUA.

scholarly journals Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252337
Author(s):  
Zhaohan Wang ◽  
Zijie Zhao ◽  
Chengxin Wang
Keyword(s):  
Random Forest ◽  
Influencing Factors ◽  
Carbon Emissions ◽  
Regional Differences ◽  
Yangtze River ◽  
Carbon Emission ◽  
Machine Learning Algorithms ◽  
Population Characteristics ◽  
The Yangtze River ◽  
Real World Data

China became the country with the largest global carbon emissions in 2007. Cities are regional population and economic centers and are the main sources of carbon emissions. However, factors influencing carbon emissions from cities can vary with geographic location and the development history of the cities, rendering it difficult to explicitly quantify the influence of individual factors on carbon emissions. In this study, random forest (RF) machine learning algorithms were applied to analyze the relationships between factors and carbon emissions in cities using real-world data from Chinese cities. Seventy-three cities in three urban agglomerations within the Yangtze River Economic Belt were evaluated with respect to urban carbon emissions using data from regional energy balance tables for the years 2000, 2007, 2012, and 2017. The RF algorithm was then used to select 16 prototypical cities based on 10 influencing factors that affect urban carbon emissions while considering five primary factors: population, industry, technology levels, consumption, and openness to the outside world. Subsequently, 18 consecutive years of data from 2000 to 2017 were used to construct RFs to investigate the temporal predictability of carbon emission variation in the 16 cities based on regional differences. Results indicated that the RF approach is a practical tool to study the connection between various influencing factors and carbon emissions in the Yangtze River Economic Belt from different perspectives. Furthermore, regional differences among the primary carbon emission influencing factors for each city were clearly observed and were related to urban population characteristics, urbanization level, industrial structures, and degree of openness to the outside world. These factors variably affected different cities, but the results indicate that regional emission reductions have achieved positive results, with overall simulation trends shifting from underestimation to overestimation of emissions.

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