scholarly journals Research on the Coordination of Environmental Pollution and Industrial Structure Upgrading from the Coupling Perspective

2021 ◽  
Vol 290 ◽  
pp. 03015
Author(s):  
Wenjing Zhang
Keyword(s):  
Energy Consumption ◽  
Environmental Pollution ◽  
Industrial Structure ◽  
Economic Transformation ◽  
Coordinated Development ◽  
Environmentally Sustainable ◽  
Inaccurate Data ◽  
Vec Model ◽  
Endogenous Variables ◽  
Industrial Structure Upgrading

Economic transformation and industrial structure upgrading are critical to achieving environmentally sustainable development. Based on the inaccurate data analysis of the VEC model and the logarithmic average exponential decomposition method, this paper proposes an analysis of the two-way coupling relationship between natural gas energy consumption and environmental pollution. Through the identification of conceptual models of the re-linking and decoupling relationship between energy consumption and environmental pollution, two-dimensional endogenous variables and exogenous variables are determined. The research results show that both energy consumption and CO2 emissions are on the actual data statistical line, with an error of 0. At the end, the article puts forward policy recommendations to promote the coordinated development of urban agglomeration environmental pollution and industrial structure upgrading.

Research on Energy Efficiency and Environmental Pollution in China: a Simultaneous Equations Approach

2011 ◽  
Vol 361-363 ◽  
pp. 974-977 ◽  
Author(s):  
Ying Nan Dong ◽  
Yu Duo Lu ◽  
Jiao Jiao Yu
Keyword(s):  
Energy Efficiency ◽  
Sustainable Development ◽  
Energy Consumption ◽  
Environmental Pollution ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Economic Loss ◽  
Simultaneous Equations ◽  
Consumption Structure ◽  
The Relationship

This paper examined the relationship between the energy efficiency and the environmental pollution. By using the data of energy intensity and economic loss caused by environmental pollution (ELP) in China from 1989-2009, a simultaneous equations was developed. The result of two-stage OLS estimation suggested that the energy had exerted positive influences on the decreasing of the environmental pollutions. By enhancing the energy efficiency and adjusting the industrial structure and energy consumption structure, China is exploring a road for sustainable development in the energy conservation.

scholarly journals Research on the spatial effect and threshold effect of industrial structure upgrading on carbon emissions in China

2021 ◽  
Author(s):  
Zhenqiang Li ◽  
Qiuyang Zhou
Keyword(s):  
Energy Consumption ◽  
Population Size ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Threshold Effect ◽  
Local Areas ◽  
The Impact ◽  
Consumption Intensity ◽  
Industrial Structure Upgrading

Abstract Based on panel data from 2000 to 2017 in 29 Chinese provinces, this paper analyzes the impact of industrial structure upgrading on carbon emissions by constructing a spatial panel model and a panel threshold model. The results show that (1) there is a significant spatial correlation between carbon emissions in Chinese provinces, and the carbon emissions of a province are affected by the carbon emissions of surrounding provinces; (2) in China, carbon emissions have a significant time lag feature, and current carbon emissions are largely affected by previous carbon emissions; (3) industrial structure upgrading can effectively promote carbon emission reductions in local areas, and the impact of industrial structure upgrading on carbon emissions has a significant threshold effect. With continued economic development, the promotion effect of industrial structure upgrading on carbon emission reductions will decrease slightly, but this carbon emission reduction effect is still significant. (4) In addition, there is a clear difference between the impact of energy consumption intensity and population size on carbon emissions in short and long terms. In the short term, the increase in energy consumption intensity and the expansion of population size not only increase the carbon emissions of a local area but also increase the carbon emissions of neighboring areas. In the long term, the impact of energy consumption intensity and population size on carbon emissions of neighboring areas will be weakened, but the promotion impact on carbon emissions in local areas will be strengthened.

scholarly journals Spatiotemporal Trajectory of China’s Provincial Energy Efficiency and Implications on the Route of Economic Transformation

2018 ◽  
Vol 10 (12) ◽  
pp. 4582 ◽  
Author(s):  
Chao Xu ◽  
Yunpeng Wang ◽  
Lili Li ◽  
Peng Wang
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Growth Rates ◽  
Industrial Structure ◽  
High Energy ◽  
Economic Transformation ◽  
Trajectory Clustering ◽  
Long Period ◽  
Per Capita ◽  
Spatiotemporal Trajectories

A comparative analysis of the spatiotemporal trajectory of energy efficiency (STEE) among the provinces in China over the past 21 years was conducted based on a quadrant diagram of the GDP per capita and the energy consumption per capita. An energy macro-efficiency per capita indicator (EMEPCI) was established using the energy consumption data of 30 Chinese provinces from 1996 to 2016. The spatiotemporal trajectory clustering method (STCM) and the industrial structure upgrading index (ISUI) were used for an exploratory analysis of the driving force of the changes in the STEE. The results showed the following: (1) The growth rate and amplitude of energy efficiency differed by province. From a geospatial perspective, the energy efficiency of the eastern regions was higher than that of the western regions, and that of the southern regions was higher than that of the northern regions. The growth trends demonstrated a pattern in which the provinces with higher energy efficiency had higher growth rates, whereas the provinces with lower energy efficiency showed lower growth rates. (2) The majority of the Chinese provinces, particularly the southwest region and the regions near the middle stream of the Yangtze River, were still undergoing a development process. Thus, it is necessary to pay attention to the adjustment of the economic development model to avoid shifting towards quadrants I or II, where the energy consumption is higher. (3) A spatiotemporal trajectory clustering analysis grouped the different provinces into four categories. Besides the majority of the provinces, which remained in quadrant III, Beijing, Shanghai, and Tianjin have remained in the “dual-high” quadrant for long period of time and are shifting towards quadrant IV. The trajectory of the second group was characterized by movement almost directly from the “dual-low” quadrant (III) towards the target quadrant (IV). The common feature of the energy efficiency trajectory of the third group was that they remained in the high energy consumption and low GDP quadrant for a relatively long period, and immediate changes were required in their development models. (4) The provinces with a similar industrial structure transformation level were more likely to have similar spatiotemporal trajectories of energy efficiency. Particularly, provinces with a similar level of transformation from secondary industries to tertiary industries enjoyed a greater probability of transformation as well as similar spatiotemporal trajectories of energy efficiency.

scholarly journals An Empirical Study on Dynamic Evolution of Industrial Structure and Green Economic Growth—Based on Data from China’s Underdeveloped Areas

2021 ◽  
Vol 13 (15) ◽  
pp. 8154
Author(s):  
Gefu Liang ◽  
Dajia Yu ◽  
Lifei Ke
Keyword(s):  
Economic Growth ◽  
Total Factor Productivity ◽  
Industrial Development ◽  
Industrial Structure ◽  
Developed Countries ◽  
Economic Transformation ◽  
Factor Productivity ◽  
Underdeveloped Areas ◽  
The Relationship ◽  
Industrial Structure Upgrading

From the experiences of developed countries or areas, advanced industrial structure is an effective way to promote economic transformation and high-quality growth. This paper uses the economic development data of seven underdeveloped provinces in China in 10 years to study the relationship between industrial structure upgrading, industrial structure rationalization and green economic growth. The result shows: (1) The relationship between the upgrading of industrial structure and green total factor productivity (GTFP) is a non-linear relationship that is difficult to fit. (2) There are two turning points in the relationship curve between industrial structure upgrading and green total factor productivity (these can be called “rationalization points”). (3) The “rationalization points” are affected by the rationalization of the industrial structure. (4) The “rationalization point” divides the relationship curve into three intervals. Within the threshold range [0.661, 0.673] of the rationalization of the industrial structure, the upgrading of the industrial structure promotes the increase of green total factor productivity, while outside the range, the upgrading of the industrial structure inhibits the increase of green total factor productivity. Therefore, industrial development in underdeveloped areas should first implement rationalization of industrial structure. After the rational adjustment of the industrial structure, we will then develop a high-level industrial structure to improve the green TFP.

Coordinated development of industrial structure and energy structure in China: its measurement and impact on CO2 emissions

2020 ◽  
Vol 81 ◽  
pp. 29-42
Author(s):  
J Zhao ◽  
C Dong ◽  
X Dong ◽  
Q Jiang
Keyword(s):  
Energy Consumption ◽  
Industrial Structure ◽  
Measurement Model ◽  
Ordinary Least Squares ◽  
Energy Structure ◽  
Total Energy Consumption ◽  
Coordinated Development ◽  
Secondary Industry ◽  
Development Levels

This study aims to explore the coordinated development of energy and industrial structures in China and their influence on the country’s inter-provincial CO2 emissions. The study utilizes an unbalanced panel dataset for 30 provinces in China covering 1995-2014 and, based on this, constructs an index system and measurement model of the coordinated development of industrial and energy structures. Considering the stationarity and cointegration of the variables, a series of econometric techniques are employed. At the same time, panel fully modified- and dynamic ordinary least squares (FMOLS and DOLS, respectively) models are used to estimate the long-term parameters of all variables. The overall estimations imply that the coordinated development levels of the dual structures show fluctuating trends, and are mainly at a low coordinated level (50-85%). The coordinated development degree of the dual structures can lead to a decline in CO2 emissions at the provincial level. The key driver is total energy consumption, followed by, in order of their impacts on CO2 emissions, fossil energy consumption, secondary industry ratio, and total population of the provinces and dual structure collaboration. However, the results indicate varied performance among the variables across regions. Finally, corresponding policy recommendations are proposed.

Judgment of Resources-Environment-Economy Coordinative Efficiency in China on DEA Model

2011 ◽  
Vol 225-226 ◽  
pp. 1177-1182
Author(s):  
Bo Jin
Keyword(s):  
Economic Development ◽  
Energy Consumption ◽  
Environmental Pollution ◽  
Relative Efficiency ◽  
Scale Efficiency ◽  
Analysis Model ◽  
Coordinated Development ◽  
Consumption Structure ◽  
Dea Analysis ◽  
Good Mode

If the resources-environment-economy coordinated development is in good mode in China？In this article, DMU were various provinces, municipalities and autonomous regions. Operating for our DEA analysis model using math software LINGO8.0, we obtained the DEA relative efficiency value of every DMU including scale efficiency and technology efficiency.We consider reducing environmental pollution under the premise of ensuring economic development must to adjust their energy consumption structure.

scholarly journals Impacts of Foreign Direct Investment and Industrial Structure Transformation on Haze Pollution across China

2021 ◽  
Vol 13 (10) ◽  
pp. 5439
Author(s):  
Chenggang Li ◽  
Tao Lin ◽  
Zhenci Xu ◽  
Yuzhu Chen
Keyword(s):  
Foreign Direct Investment ◽  
Environmental Pollution ◽  
Direct Investment ◽  
Industrial Structure ◽  
Spatial Data Analysis ◽  
Annual Data ◽  
Eastern Region ◽  
Structure Transformation ◽  
Haze Pollution ◽  
The Impact

With the development of economic globalization, some local environmental pollution has become a global environmental problem through international trade and transnational investment. This paper selects the annual data of 30 provinces in China from 2000 to 2017 and adopts exploratory spatial data analysis methods to explore the spatial agglomeration characteristics of haze pollution in China’s provinces. Furthermore, this paper constructs a spatial econometric model to test the impact of foreign direct investment (FDI) and industrial structure transformation on haze pollution. The research results show that the high-high concentration area of haze pollution in China has shifted from the central and western regions to the eastern region and from inland regions to coastal regions. When FDI increases by 1%, haze pollution in local and neighboring areas will be reduced by 0.066% and 0.3538%, respectively. However, the impact of FDI on haze pollution is heterogeneous in different stages of economic development. FDI can improve the rationalization level of industrial structure, and then inhibit the haze pollution. However, FDI inhibits the upgrading level of industrial structure to a certain extent, and then aggravates the haze pollution. The research in this paper provides an important decision-making basis for coordinating the relationship between FDI and environmental pollution and realizing green development.

scholarly journals An Improved Forecasting Method and Application of China’s Energy Consumption under the Carbon Peak Target

2021 ◽  
Vol 13 (15) ◽  
pp. 8670
Author(s):  
Xiwen Cui ◽  
Shaojun E ◽  
Dongxiao Niu ◽  
Dongyu Wang ◽  
Mingyu Li
Keyword(s):  
Sustainable Development ◽  
Energy Consumption ◽  
Environmental Pollution ◽  
Optimization Methods ◽  
Support Vector ◽  
The Sustainable Development ◽  
Consumption Data ◽  
Forecasting Method ◽  
Forecasting Performance ◽  
Carbon Peak

In the process of economic development, the consumption of energy leads to environmental pollution. Environmental pollution affects the sustainable development of the world, and therefore energy consumption needs to be controlled. To help China formulate sustainable development policies, this paper proposes an energy consumption forecasting model based on an improved whale algorithm optimizing a linear support vector regression machine. The model combines multiple optimization methods to overcome the shortcomings of traditional models. This effectively improves the forecasting performance. The results of the projection of China’s future energy consumption data show that current policies are unable to achieve the carbon peak target. This result requires China to develop relevant policies, especially measures related to energy consumption factors, as soon as possible to ensure that China can achieve its peak carbon targets.

scholarly journals Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 196 ◽  
Author(s):  
Lihui Zhang ◽  
Riletu Ge ◽  
Jianxue Chai
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Tabu Search ◽  
Industrial Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Forecasting Model ◽  
Robust Principal Component Analysis ◽  
Consumption Structure

China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.

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