Time and space model of urban pollution migration: Economy-energy-environment nexus network

Liu, Gengyuan; Fath, Brian D.; Shi, Lei; Ulgiati, Sérgio

doi:10.1016/j.apenergy.2016.06.132

Cited by 37 publications

(15 citation statements)

References 28 publications

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“…The construction of spatial association network is the basis of the application of social network theory and method. Gravity model and VAR model are the two most commonly used methods to determine spatial association network in current research [ 74 ]. The VAR model focuses on the hysteresis of variables, while the gravity model is more suitable for the measurement of gravity between regions.…”

Section: Methodsmentioning

confidence: 99%

Analysis on the Spatial-Temporal Evolution Characteristics and Spatial Network Structure of Tourism Eco-Efficiency in the Yangtze River Delta Urban Agglomeration

Sun

Hou

2021

IJERPH

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Based on the panel data of 41 cities in the Yangtze River Delta from 2008 to 2017, this paper constructs an evaluation indicators system for urban tourism eco-efficiency. By measuring the tourism eco-efficiency in the Yangtze River Delta urban agglomeration, we analyze its spatial-temporal evolution characteristics. Furthermore, the modified gravity model and social network analysis are introduced to explore the spatial network structure of tourism eco-efficiency and its evolution trend.The results show that:(1) The overall eco-efficiency of tourism in the Yangtze River Delta region presents a fluctuating downward trend, among which Jiangsu and Zhejiang have high eco-efficiency, Shanghai and Anhui are relatively low. The gap within the region first increased and then decreased. (2) During this decade, the spatial network structure of tourism eco-efficiency in the Yangtze River Delta has become increasingly loose. The weakening of the network connection strength has led to a decrease in the regional tourism eco-efficiency to a great extent. (3) The network centrality of cities such as Zhoushan, Huzhou, and Huangshan has always maintained a high level, and these cities have firmly occupied the core position of network. (4) The spatial association network of tourism eco-efficiency can be divided into four blocks: “two-way spillover”, “net spillover”, “net benefit” and “agent”. The synergy and spillover effect between various blocks are significant, and there is a spatial polarization trend centered on a few cities. Based on this, this paper puts forward optimization suggestions for the spatial network structure of the Yangtze River Delta urban agglomeration, in anticipation of promoting the improvement of regional tourism eco-efficiency.

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Section: Methodsmentioning

confidence: 99%

Analysis on the Spatial-Temporal Evolution Characteristics and Spatial Network Structure of Tourism Eco-Efficiency in the Yangtze River Delta Urban Agglomeration

Sun

Hou

2021

IJERPH

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“…As mentioned above, we assumed that the effects of R and D on carbon emissions may be influenced by the development level of the area. Considering relative studies, this paper chooses urbanization, the ratio of urban population to the total, to denote the level of development of each region [32,33].…”

Section: Threshold Variablementioning

confidence: 99%

Is There Any Difference in the Effect of Different R and D Sources on Carbon Intensity in China?

Feng

Peng

2019

Sustainability

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In recent decades, climate change, mostly caused by CO2 emissions, has become a critical issue of concern to people worldwide. It is necessary for countries all around the world to reduce carbon emissions. China, as the world’s largest carbon emitter, is under great pressure to implement carbon-reduction strategies. Technological progress plays a crucial role in balancing environmental and economic development. The main objective of this work is to empirically compare the effects of government and enterprise research and development (R and D) on carbon-emission reduction using the panel data of 30 Chinese provinces from 2009 to 2016. The effects of both government and enterprise R and D investment on carbon intensity are compared in detail through a linear model and a threshold-regression model. Linear-regression results shows that both government and enterprise R and D decrease carbon intensity, while enterprise investment tends to be more instant. Further threshold-regression results indicate that the effects of government and enterprise R and D on carbon intensity are different in different urbanization stages. Guiding enterprises to invest in R and D in medium-developing areas, and increasing government support and subsidies for R and D activities in underdeveloped areas should be an important goal of the government policies.

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“…In recent years, some authors attend to build energy-economy-environment models have increased gradually as well [27][28][29][30]. However, there are few studies on the dynamic coordinated development of the energy structure and industrial structure.…”

Section: Literature Reviewmentioning

confidence: 99%

Projections and Recommendations for Energy Structure and Industrial Structure Development in China through 2030: A System Dynamics Model

et al. 2019

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In this research, we established a System Dynamics Model named “E&I-SD” to study the development of the energy structure and industrial structure in China from 2000 to 2030 using Vensim Simulation Software based on energy economy theory, system science theory and coordinated development theory. We used Direct Structure Test, Structure-oriented Behavior Test, and Behavior Pattern Test to ensure the optimal operation of the system. The model’s results showed that the indicators of total energy consumption, total added value of GDP after regulation, energy consumption per capita, and GDP per capita were on the rise in China, but emissions per unit of energy showed a downward trend. Separately, the model predicted average annual growth rates in China through 2030. Based on these findings, we proposed important policies for China’s sustainable development. Firstly, short- and long-term policy measures should be implemented to replace fossil fuels with clean energy. Secondly, the utilization efficiency of raw coal should be appraised future. The planning should provide for steady development and improvement of the primary, secondary, and tertiary sectors. Thirdly, the mid- and long-term plans for development and management of various industrial sectors and the corresponding energy consumption should be based on technological trends. Finally, a market-oriented pricing mechanism for energy should be established in China as soon as possible.

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Time and space model of urban pollution migration: Economy-energy-environment nexus network

Cited by 37 publications

References 28 publications

Analysis on the Spatial-Temporal Evolution Characteristics and Spatial Network Structure of Tourism Eco-Efficiency in the Yangtze River Delta Urban Agglomeration

Analysis on the Spatial-Temporal Evolution Characteristics and Spatial Network Structure of Tourism Eco-Efficiency in the Yangtze River Delta Urban Agglomeration

Is There Any Difference in the Effect of Different R and D Sources on Carbon Intensity in China?

Projections and Recommendations for Energy Structure and Industrial Structure Development in China through 2030: A System Dynamics Model

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